The document describes an automatic recommendation scheme for personalized IPTV services. It presents a scheme that uses collaborative filtering to recommend TV program contents to users based on their viewing histories, without requiring explicit ratings from users. The scheme clusters users into groups based on their genre preferences inferred from viewing histories. It then obtains candidate programs for an active user via collaborative filtering within their user group. The candidates are ranked using a novel ranking model to produce the top recommendations. Experimental results showed the approach achieved average precision of about 77% for top-5 recommendations.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
This paper develops neural network models that can predict user quality of experience (QoE) for Internet Protocol television (IPTV) applications in real time based on network measurements. The models account for multiple video resolutions, audio/video codecs, and network conditions including jitter, packet loss, and router queuing disciplines. The models were trained using data from objective network simulations and subjective human experiments evaluating mean opinion scores for quality. Evaluation shows the models accurately and quickly predict user QoE for IPTV under different conditions and can be used to monitor network quality in real-time.
consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which the criteria of video attribute that important are. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users.
This document summarizes a research paper on performance analysis of spectrum sharing in cognitive radio using a common control channel. It discusses how cognitive radio can dynamically detect unused spectrum and allocate it to secondary users without interfering with primary users. The paper presents simulation results demonstrating the spectrum sharing process. It shows unused spectrum being allocated to new secondary users, and secondary users vacating the spectrum if a primary user needs it. The effects of noise and signal attenuation on the spectrum sharing are also analyzed through the simulation results.
Parental control for TV viewers is an open field as many efforts were provided to provide trusted solutions for this issue. Although this technology was improved and developed, but it still leaks a good and robust solution as it is related to critical age of viewers. This paper introduce a new hybrid technology that depends on the information transmitted along with the Audio and Video (EPG), also extra resources from internet were used to enhance the decision in the EPG engine in the proposed system. All viewers enrolled in the system were successfully authenticated / de-authenticated depending on their registered age in the database and on the age related for each program.
Resource Allocation in MIMO – OFDM Communication System under Signal Strength...Kumar Goud
Abstract: - Multiple Inputs and Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) system have the potential to attain high capability on the propagation setting. The aim of this paper is that the adaptive resource allocation in MIMO-OFDM system uses the water filling formula. Water filling answer is enforced for allocating the ability so as to extend the data rate. The overall system capability is maximised subject to the constraints on total power, signal to noise quantitative relation, and proportionality. Channel is assumed as a flat attenuation channel and therefore the comparison is created for various 2×2, 2×3, 3×2 and 4×4 MIMO-OFDM systems and water filling formula with allotted power. Supported the capability contribution from the relaying terminal, a brand new parameter referred to as cooperation constant is introduced as an operate of the relaying sub channel. This parameter is employed to switch the target parameter of the subcarrier allocation procedure. Fairness-oriented [Fading Channel] and throughput-oriented [Near finish Channel] algorithms square measure elite from the literature to check the planned technique. Each algorithms square measure changed to use the mean of cooperation constant within the objective parameter of the subcarrier allocation procedure and shown to own a much better total turnout with none sacrifice.
Keywords - MIMO-OFDM; Water filling Algorithm; Subcarrier Resource Allocation
This document provides a survey of cooperative communication techniques for 4G LTE wireless networks. It discusses how cooperative communication can provide benefits like increased capacity, transmission reliability, and spatial diversity without needing multiple antennas at both transmitter and receiver. The document reviews various cooperative relaying approaches including amplify-and-forward and decode-and-forward. It also compares techniques like DF cooperative relaying, DNF-TPSR, STBC-NOMA, and discusses issues like spectral efficiency that future research could focus on improving cooperative communication further.
Using Bandwidth Aggregation to Improve the Performance of Video Quality- Adap...paperpublications3
1. The document discusses using bandwidth aggregation to improve video streaming quality over multiple wireless access networks. It formulates the multi-link video streaming process as a reinforcement learning task using a Markov Decision Process to determine optimal actions.
2. It proposes a quality-adaptive streaming approach that uses an adaptive search algorithm to obtain sub-optimal solutions in real time. The approach aims to achieve smooth, high quality video streaming while avoiding interruptions.
3. The approach considers metrics like startup latency, playback fluency, average playback quality, playback smoothness, and wireless service cost in its reward function to balance video quality of service with cost. It was evaluated using a realistic testbed with an Android phone.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
This paper develops neural network models that can predict user quality of experience (QoE) for Internet Protocol television (IPTV) applications in real time based on network measurements. The models account for multiple video resolutions, audio/video codecs, and network conditions including jitter, packet loss, and router queuing disciplines. The models were trained using data from objective network simulations and subjective human experiments evaluating mean opinion scores for quality. Evaluation shows the models accurately and quickly predict user QoE for IPTV under different conditions and can be used to monitor network quality in real-time.
consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which the criteria of video attribute that important are. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users.
This document summarizes a research paper on performance analysis of spectrum sharing in cognitive radio using a common control channel. It discusses how cognitive radio can dynamically detect unused spectrum and allocate it to secondary users without interfering with primary users. The paper presents simulation results demonstrating the spectrum sharing process. It shows unused spectrum being allocated to new secondary users, and secondary users vacating the spectrum if a primary user needs it. The effects of noise and signal attenuation on the spectrum sharing are also analyzed through the simulation results.
Parental control for TV viewers is an open field as many efforts were provided to provide trusted solutions for this issue. Although this technology was improved and developed, but it still leaks a good and robust solution as it is related to critical age of viewers. This paper introduce a new hybrid technology that depends on the information transmitted along with the Audio and Video (EPG), also extra resources from internet were used to enhance the decision in the EPG engine in the proposed system. All viewers enrolled in the system were successfully authenticated / de-authenticated depending on their registered age in the database and on the age related for each program.
Resource Allocation in MIMO – OFDM Communication System under Signal Strength...Kumar Goud
Abstract: - Multiple Inputs and Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) system have the potential to attain high capability on the propagation setting. The aim of this paper is that the adaptive resource allocation in MIMO-OFDM system uses the water filling formula. Water filling answer is enforced for allocating the ability so as to extend the data rate. The overall system capability is maximised subject to the constraints on total power, signal to noise quantitative relation, and proportionality. Channel is assumed as a flat attenuation channel and therefore the comparison is created for various 2×2, 2×3, 3×2 and 4×4 MIMO-OFDM systems and water filling formula with allotted power. Supported the capability contribution from the relaying terminal, a brand new parameter referred to as cooperation constant is introduced as an operate of the relaying sub channel. This parameter is employed to switch the target parameter of the subcarrier allocation procedure. Fairness-oriented [Fading Channel] and throughput-oriented [Near finish Channel] algorithms square measure elite from the literature to check the planned technique. Each algorithms square measure changed to use the mean of cooperation constant within the objective parameter of the subcarrier allocation procedure and shown to own a much better total turnout with none sacrifice.
Keywords - MIMO-OFDM; Water filling Algorithm; Subcarrier Resource Allocation
This document provides a survey of cooperative communication techniques for 4G LTE wireless networks. It discusses how cooperative communication can provide benefits like increased capacity, transmission reliability, and spatial diversity without needing multiple antennas at both transmitter and receiver. The document reviews various cooperative relaying approaches including amplify-and-forward and decode-and-forward. It also compares techniques like DF cooperative relaying, DNF-TPSR, STBC-NOMA, and discusses issues like spectral efficiency that future research could focus on improving cooperative communication further.
Using Bandwidth Aggregation to Improve the Performance of Video Quality- Adap...paperpublications3
1. The document discusses using bandwidth aggregation to improve video streaming quality over multiple wireless access networks. It formulates the multi-link video streaming process as a reinforcement learning task using a Markov Decision Process to determine optimal actions.
2. It proposes a quality-adaptive streaming approach that uses an adaptive search algorithm to obtain sub-optimal solutions in real time. The approach aims to achieve smooth, high quality video streaming while avoiding interruptions.
3. The approach considers metrics like startup latency, playback fluency, average playback quality, playback smoothness, and wireless service cost in its reward function to balance video quality of service with cost. It was evaluated using a realistic testbed with an Android phone.
This document discusses cooperative wireless communications. It introduces cooperative communications where users help each other by relaying signals. Some of the research problems discussed include evaluating the performance of cooperative networks, improving current techniques, and finding new techniques to achieve better performance. The document outlines cooperative communications and research challenges, such as channel estimation and the effect of user mobility.
Dynamic Sub-Channel Allocation in Multiuser OFDM Systems to Achieve Variable ...IDES Editor
This document summarizes a research paper that proposes a dynamic sub-channel allocation algorithm in multiuser OFDM systems to achieve variable data rates. It investigates allocating frequency sub-channels adaptively among users to take advantage of channel diversity and maximize capacity while maintaining proportional fairness. The algorithm first allocates sub-channels assuming equal power distribution, then optimizes power allocation. Simulation results show the algorithm efficiently distributes capacity according to proportional rate constraints.
The document discusses various television transmission technologies including direct-to-home (DTH) systems, digital cable, internet protocol television (IPTV), and mobile apps for viewing television. It provides details on how DTH systems work by transmitting signals directly from satellites to receivers, the components involved, and growth of DTH in India. It also compares digital cable and DTH, describes technologies like headend in the sky (HITS) and IPTV, and discusses options for viewing television through devices like Fire TV sticks and mobile applications.
T Silva, D D Karunaratna, G N Wikramanayake, K P Hewagamage, G K A Dias (2004) "Speaker Search and Indexing for Multimedia Databases" In:6th International Information Technology Conference, Edited by:V.K. Samaranayake et al. pp. 157-162. Infotel Lanka Society, Colombo, Sri Lanka: IITC Nov 29-Dec 1, ISBN: 955-8974-01-3
Performance Analysis of Wireless Networks With MDQOSIJERA Editor
In Wireless/Mobile networks various kinds of encoding schemes were used for transmission of data over a bandwidth. The desired quality and generated traffic varies with the requirement with this bandwidth. A generic video telephony may require more than 40 kbps whereas a low motion video telephony may require about 25 kbps for data transmission. From the designing point of view these requirements demands for an alternative resource planning, especially for bandwidth allocation in wireless networks. In wireless network where bandwidth is a scare resource, the system may need to block incoming user if all of the bandwidth has been used to provide highest quality of service to existing users. However this bandwidth resource planning may be unacceptable for larger application. A degradable approach to multiple users can be made on bandwidth allocation to reduce the blocking probability without degrading the quality of service to existing users.
This work aims towards a realization of a wireless/mobile network using W-CDMA multi access technique supporting multilevel quality of services. The bandwidth allocation to multiple users is adjusted dynamically according to the required network condition so as to increase bandwidth utilization. The work analyze the performance deriving the degradation period ratio, mean degradation time and degradation state for the implemented wireless network.The proposed work is aim to implement on Matlab tool for its functional verification considering various mobility patterns
This document proposes a bandwidth degradation technique to reduce call dropping probability in mobile
networks. It aims to dynamically adjust bandwidth allocation to multiple users according to network conditions
to increase utilization. The technique allows for degrading the quality of existing calls to admit new calls
while maintaining quality of service. Key performance metrics analyzed include degradation ratio, degraded
bandwidth, throughput, and propagation delay. The approach is intended to be implemented using MATLAB
to simulate various mobility patterns for verification.
Opportunistic and playback sensitive scheduling for video streamingijwmn
Given the strict Quality of Service (QoS) requirements of video streaming, this paper proposes a novel
solution for simultaneous streaming of multiple video sessions over a mobile cellular system. The proposed
solution combines a buffer management strategy with a packet scheduling algorithm. The buffer
management strategy selectively discards packets of a user from base station buffer whereas the packet
scheduling algorithm schedules packets of a user according to its instantaneous channel quality, average
throughput and playback buffer information. Simulation results demonstrate that the proposed solution is
effective in providing a continuous video playback with good perceptual quality for more users. If at least a
good perceptual quality is to be satisfied for all users (QoS constraint of video streaming), then the
proposed solution improves the system capacity by 40% over a conventional packet scheduling algorithm.
How to Find Winning Traders - Perspective from 2 Stock Market ExpertsChaikin Analytics
Join stock market veterans, Marc Chaikin and Harry Boxer, discuss how to identify winning trades in today's market. Marc is CEO and Founder of Chaikin Analytics and Harry Boxer is the founder of TheTechTrader.com
Optimization involves shrinking file sizes, prioritizing relevant content, and creating efficient layouts to encourage faster loading times and keep users engaged on websites. Key techniques include using stylesheets instead of embedded CSS, positioning non-essential code lower on pages, splitting large tables and content across multiple pages, and loading media separately rather than embedding it. The goal is for users to access important information quickly while other content loads in the background.
This document provides an overview of government information and how it can be accessed. It discusses that government information is produced by federal, state, local, international and non-governmental organizations. It also outlines some key characteristics of government information, such as it being authoritative and produced as a result of legal requirements or grant-funded research. Additionally, the document discusses tools that can be used to find government information, such as reference interviews, agency websites, catalogs and the Freedom of Information Act.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document describes an investing strategy called "Upgrades and Revisions 2" that has historically generated strong returns. The strategy screens for stocks that have received positive earnings estimate revisions and rating upgrades in recent weeks. It uses several other fundamental metrics to identify stocks with strong earnings growth, reasonable valuation, and a track record of meeting or beating earnings expectations. Backtesting shows the strategy has averaged about 8-10 stock picks per month, with a high win rate of 74% and market-beating returns over many years.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
1) The document discusses a recommender system called TV Advisor that recommends TV programs to users based on their preferences. It analyzes questions around profiling users, information filtering methods, and generating recommendations for TV.
2) TV Advisor gathers information on TV programs from online sources and allows users to provide preferences on categories like movies/drama to create a profile. It uses cognitive filtering and the user profile to select recommended programs to watch or record.
3) An initial version of TV Advisor found that cognitive filtering using a tree of likes/dislikes worked well to provide relevant recommendations. It also needed to always provide recommendations even if no good matches were found to the user's profile.
Cccnc using content-based filtering in a system of recommendation in the co...Elaine Cecília Gatto
Recommendation systems provide suggestions based on information about the preferences of users. The filtering information is used by recommender systems for the processing of information and suggestions to users and content-based filtering is an approach to filtering information widely used in recommender systems. Content-Based Filtering on analyzing the correlation of the content of items with the profile, suggesting relevant items and discarding the irrelevant. Widely used on the Internet, recommendation systems are being studied for use in the context of Digital TV, there are already several studies in this direction. Just as occurs on the Internet, recommendation systems can be used in Digital TV for recommendation of TV programs, advertising and publicity and also electronic commerce. Thus, the items in the context of digital TV, may be programs, publicity / advertising and the products to be sold. Applying Content Filtering Based on the recommendation of programs, for example, it should correlate the content of these programs with user preferences, which in this scenario are the types of programs he has preferred to watch. This paper presents the studies performed with Content Filtering Based on Data Applied to Digital TV. The studies seek to observe and evaluate how some techniques of content-based filtering can be used in recommendation systems in the context of Digital TV
Video contents prior storing server forIJCNCJournal
This document proposes a new architecture for multicasting live IPTV traffic in optical access networks. It involves assigning a unique logical link identifier (CLLID) to each IPTV channel. A prior storing server is constructed in the optical line terminal (OLT) and each optical network unit (ONU) to manage multicasting. The document proposes a partial prior storing strategy that considers changes in video content segment popularity over time and user access patterns to compute stored object utility. It also proposes partitioning the prior storage to avoid popular objects being evicted by unpopular objects accessed with higher frequency. Simulation results show the proposed architecture can improve performance and quality of service parameters.
Using content-based filtering in a system of recommendation in the context of...Elaine Cecília Gatto
This document discusses content-based filtering for recommendation systems in the context of digital mobile interactive television (IDTV). It presents studies on applying content-based filtering techniques to recommend TV programs and advertisements in digital TV. The recommendation system analyzes a user's viewing history to determine their preferences and profile, then filters content like programs and ads in the electronic programming guide (EPG) to find items that match the user's profile and suggest them. The system aims to help users more easily find content they are interested in on digital TV and mobile devices.
Application of recommendation techniques for brazilian portable interactive d...Elaine Cecília Gatto
The document discusses recommendation techniques for Brazilian portable interactive digital TV (IDTV). It compares IDTV in residences versus cell phones, noting differences in usage and screen size. It reviews related recommendation systems and then briefly describes the authors' system, which uses user viewing history and electronic programming guide data to provide personalized TV program recommendations on cell phones without server communication. The system was designed according to Brazilian IDTV standards for portable devices.
The document discusses enhanced or interactive television (ETV) and the components involved in creating and delivering ETV applications. It describes ETV as video programming with an interactive application bound to it. Key components discussed include ETV application programs and data, signaling, stream events/triggers, media timeline, and application servers. It also outlines the process of authoring ETV applications and distributing them through various networks.
This document discusses cooperative wireless communications. It introduces cooperative communications where users help each other by relaying signals. Some of the research problems discussed include evaluating the performance of cooperative networks, improving current techniques, and finding new techniques to achieve better performance. The document outlines cooperative communications and research challenges, such as channel estimation and the effect of user mobility.
Dynamic Sub-Channel Allocation in Multiuser OFDM Systems to Achieve Variable ...IDES Editor
This document summarizes a research paper that proposes a dynamic sub-channel allocation algorithm in multiuser OFDM systems to achieve variable data rates. It investigates allocating frequency sub-channels adaptively among users to take advantage of channel diversity and maximize capacity while maintaining proportional fairness. The algorithm first allocates sub-channels assuming equal power distribution, then optimizes power allocation. Simulation results show the algorithm efficiently distributes capacity according to proportional rate constraints.
The document discusses various television transmission technologies including direct-to-home (DTH) systems, digital cable, internet protocol television (IPTV), and mobile apps for viewing television. It provides details on how DTH systems work by transmitting signals directly from satellites to receivers, the components involved, and growth of DTH in India. It also compares digital cable and DTH, describes technologies like headend in the sky (HITS) and IPTV, and discusses options for viewing television through devices like Fire TV sticks and mobile applications.
T Silva, D D Karunaratna, G N Wikramanayake, K P Hewagamage, G K A Dias (2004) "Speaker Search and Indexing for Multimedia Databases" In:6th International Information Technology Conference, Edited by:V.K. Samaranayake et al. pp. 157-162. Infotel Lanka Society, Colombo, Sri Lanka: IITC Nov 29-Dec 1, ISBN: 955-8974-01-3
Performance Analysis of Wireless Networks With MDQOSIJERA Editor
In Wireless/Mobile networks various kinds of encoding schemes were used for transmission of data over a bandwidth. The desired quality and generated traffic varies with the requirement with this bandwidth. A generic video telephony may require more than 40 kbps whereas a low motion video telephony may require about 25 kbps for data transmission. From the designing point of view these requirements demands for an alternative resource planning, especially for bandwidth allocation in wireless networks. In wireless network where bandwidth is a scare resource, the system may need to block incoming user if all of the bandwidth has been used to provide highest quality of service to existing users. However this bandwidth resource planning may be unacceptable for larger application. A degradable approach to multiple users can be made on bandwidth allocation to reduce the blocking probability without degrading the quality of service to existing users.
This work aims towards a realization of a wireless/mobile network using W-CDMA multi access technique supporting multilevel quality of services. The bandwidth allocation to multiple users is adjusted dynamically according to the required network condition so as to increase bandwidth utilization. The work analyze the performance deriving the degradation period ratio, mean degradation time and degradation state for the implemented wireless network.The proposed work is aim to implement on Matlab tool for its functional verification considering various mobility patterns
This document proposes a bandwidth degradation technique to reduce call dropping probability in mobile
networks. It aims to dynamically adjust bandwidth allocation to multiple users according to network conditions
to increase utilization. The technique allows for degrading the quality of existing calls to admit new calls
while maintaining quality of service. Key performance metrics analyzed include degradation ratio, degraded
bandwidth, throughput, and propagation delay. The approach is intended to be implemented using MATLAB
to simulate various mobility patterns for verification.
Opportunistic and playback sensitive scheduling for video streamingijwmn
Given the strict Quality of Service (QoS) requirements of video streaming, this paper proposes a novel
solution for simultaneous streaming of multiple video sessions over a mobile cellular system. The proposed
solution combines a buffer management strategy with a packet scheduling algorithm. The buffer
management strategy selectively discards packets of a user from base station buffer whereas the packet
scheduling algorithm schedules packets of a user according to its instantaneous channel quality, average
throughput and playback buffer information. Simulation results demonstrate that the proposed solution is
effective in providing a continuous video playback with good perceptual quality for more users. If at least a
good perceptual quality is to be satisfied for all users (QoS constraint of video streaming), then the
proposed solution improves the system capacity by 40% over a conventional packet scheduling algorithm.
How to Find Winning Traders - Perspective from 2 Stock Market ExpertsChaikin Analytics
Join stock market veterans, Marc Chaikin and Harry Boxer, discuss how to identify winning trades in today's market. Marc is CEO and Founder of Chaikin Analytics and Harry Boxer is the founder of TheTechTrader.com
Optimization involves shrinking file sizes, prioritizing relevant content, and creating efficient layouts to encourage faster loading times and keep users engaged on websites. Key techniques include using stylesheets instead of embedded CSS, positioning non-essential code lower on pages, splitting large tables and content across multiple pages, and loading media separately rather than embedding it. The goal is for users to access important information quickly while other content loads in the background.
This document provides an overview of government information and how it can be accessed. It discusses that government information is produced by federal, state, local, international and non-governmental organizations. It also outlines some key characteristics of government information, such as it being authoritative and produced as a result of legal requirements or grant-funded research. Additionally, the document discusses tools that can be used to find government information, such as reference interviews, agency websites, catalogs and the Freedom of Information Act.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document describes an investing strategy called "Upgrades and Revisions 2" that has historically generated strong returns. The strategy screens for stocks that have received positive earnings estimate revisions and rating upgrades in recent weeks. It uses several other fundamental metrics to identify stocks with strong earnings growth, reasonable valuation, and a track record of meeting or beating earnings expectations. Backtesting shows the strategy has averaged about 8-10 stock picks per month, with a high win rate of 74% and market-beating returns over many years.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
1) The document discusses a recommender system called TV Advisor that recommends TV programs to users based on their preferences. It analyzes questions around profiling users, information filtering methods, and generating recommendations for TV.
2) TV Advisor gathers information on TV programs from online sources and allows users to provide preferences on categories like movies/drama to create a profile. It uses cognitive filtering and the user profile to select recommended programs to watch or record.
3) An initial version of TV Advisor found that cognitive filtering using a tree of likes/dislikes worked well to provide relevant recommendations. It also needed to always provide recommendations even if no good matches were found to the user's profile.
Cccnc using content-based filtering in a system of recommendation in the co...Elaine Cecília Gatto
Recommendation systems provide suggestions based on information about the preferences of users. The filtering information is used by recommender systems for the processing of information and suggestions to users and content-based filtering is an approach to filtering information widely used in recommender systems. Content-Based Filtering on analyzing the correlation of the content of items with the profile, suggesting relevant items and discarding the irrelevant. Widely used on the Internet, recommendation systems are being studied for use in the context of Digital TV, there are already several studies in this direction. Just as occurs on the Internet, recommendation systems can be used in Digital TV for recommendation of TV programs, advertising and publicity and also electronic commerce. Thus, the items in the context of digital TV, may be programs, publicity / advertising and the products to be sold. Applying Content Filtering Based on the recommendation of programs, for example, it should correlate the content of these programs with user preferences, which in this scenario are the types of programs he has preferred to watch. This paper presents the studies performed with Content Filtering Based on Data Applied to Digital TV. The studies seek to observe and evaluate how some techniques of content-based filtering can be used in recommendation systems in the context of Digital TV
Video contents prior storing server forIJCNCJournal
This document proposes a new architecture for multicasting live IPTV traffic in optical access networks. It involves assigning a unique logical link identifier (CLLID) to each IPTV channel. A prior storing server is constructed in the optical line terminal (OLT) and each optical network unit (ONU) to manage multicasting. The document proposes a partial prior storing strategy that considers changes in video content segment popularity over time and user access patterns to compute stored object utility. It also proposes partitioning the prior storage to avoid popular objects being evicted by unpopular objects accessed with higher frequency. Simulation results show the proposed architecture can improve performance and quality of service parameters.
Using content-based filtering in a system of recommendation in the context of...Elaine Cecília Gatto
This document discusses content-based filtering for recommendation systems in the context of digital mobile interactive television (IDTV). It presents studies on applying content-based filtering techniques to recommend TV programs and advertisements in digital TV. The recommendation system analyzes a user's viewing history to determine their preferences and profile, then filters content like programs and ads in the electronic programming guide (EPG) to find items that match the user's profile and suggest them. The system aims to help users more easily find content they are interested in on digital TV and mobile devices.
Application of recommendation techniques for brazilian portable interactive d...Elaine Cecília Gatto
The document discusses recommendation techniques for Brazilian portable interactive digital TV (IDTV). It compares IDTV in residences versus cell phones, noting differences in usage and screen size. It reviews related recommendation systems and then briefly describes the authors' system, which uses user viewing history and electronic programming guide data to provide personalized TV program recommendations on cell phones without server communication. The system was designed according to Brazilian IDTV standards for portable devices.
The document discusses enhanced or interactive television (ETV) and the components involved in creating and delivering ETV applications. It describes ETV as video programming with an interactive application bound to it. Key components discussed include ETV application programs and data, signaling, stream events/triggers, media timeline, and application servers. It also outlines the process of authoring ETV applications and distributing them through various networks.
BIPODITVR: brazilian interactive portable digital tv recommendation systemElaine Cecília Gatto
This document describes a recommendation system called BIPoDi TVR for Brazilian interactive portable digital television. The system focuses on recommending TV programs to users based on their preferences when viewing television on their cell phones. It was developed using the Ginga middleware and NCL language to create recommendations for portable digital TV in Brazil. The goal is to help users find programs of interest among the increasing amount of available content, as electronic program guides on small screens can be difficult to navigate.
Iwssip application of recommendation techniques for brazilian portable inte...Elaine Cecília Gatto
Before the possibility of new services and
programs, and consequently more content available
provided by Brazilian digital TV system, the users face
difficulties to select their favorite programs. The
Recommendation Systems become a tool to solve these
difficulties besides improving the interactivity between
the user and the digital television by filtering
information and personalizing the content offer. Surveys
herein present two personalization techniques, one using
vector model and the other using data mining. The aim
was to observe and evaluate how these data filtering
techniques can be used and implemented in a
recommendation system inside Digital TV context in
cell phones.
QoE-enabled big video streaming for large-scale heterogeneous clients and net...redpel dot com
This document summarizes a research paper on providing quality of experience (QoE)-enabled video streaming for heterogeneous clients and networks in smart cities. It discusses the growth of video traffic and challenges in ensuring high QoE across different devices. The paper reviews video broadcasting technologies, coding methods for scalability and flexibility, and presents a paradigm for QoE-mapped joint coding and cross-layer transmission to dynamically adapt to different devices and networks. It evaluates system performance in terms of broadcasting efficiency and discusses open areas for future research.
Sigap bi po-ditvr brazilian interactive portable digital tv recommendation ...Elaine Cecília Gatto
Using the Brazilian digital television system, the possibility of offering new services and programs, and consequently more available content, will make it difficult for the users to select their favorite programs. The Recommendation Systems become a tool to solve these difficulties and they are able to improve interactivity between the user and the digital television filtering information filtering and personalizing the content offer. This paper describes a recommendation system for Brazilian interactive portable digital television focused on the cell phone which makes this functionality possible and creates TV program recommendation according to user TV programs preferences when using television in the cell phone.
The research, final thesis for the MSc in Media Management at Bocconi, aims to define peculiar business models for web TV, still an almost unexplored ground inside the economic theory, despite technological progress and a steady growing audience made it recently so attractive in terms of investments for both entrepreneurs and advertisers.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
All Aboard! A Cross-Agency Mission Toward Cross-Platform Audience-Based VideoMediaPost
Implementing an audience-based cross-platform video strategy across a large media holding company is a long journey – and one that Omnicom Media Group has recently embarked upon. OMG’s advanced TV platform—a component of Omnicom’s Omni people-based precision marketing and insights platform—ties together audience-based TV planning, buying and measurement capabilities across data-driven linear TV, household-addressable and IP-delivered OTT platforms. Jonathan will share the vision, trajectory and some early lessons learned on the early part of this journey.
HbbTV 2.0 for LinkedTV: specification and gapsLinkedTV
This document proposes using HbbTV to implement LinkedTV, which links web content to broadcast TV programs. It discusses challenges of a live broadcast scenario and outlines a potential solution. Key points include: (1) Using HbbTV to trigger LinkedTV apps and request companion screen access. (2) Receiving annotations for the TV program via the broadcast stream or polling. (3) Managing connections and displaying concepts/links across main and companion screens synchronized to the TV program timeline. The document analyzes required HbbTV 2.0 functionality and open issues.
The Optimization of IPTV Service Through SDN In A MEC Architecture, Respectiv...CSCJournals
The aim of this paper is to present the ‘Power’ of SDN Technology and MEC Technic in improving the delivering of IPTV Service. Those days, the IPTV end –users are tremendous increased all over the world , but in the same time also the complains for receiving these prepaid real time multimedial services like; high latency, high bandwidth, low performance and low QoE/QoS. On the other end, IPTV Distributors need a new system, technics, network solutions to distribute content continuesly and simultaneously to all active end-users with high-quality, lowlatency and high Performance, thus monitoring and re-configuring this ‘Big Data’ require high Bandwidth by causing difficult problems by offering it affecting in the same time the price and QoE/QoSperformance of delivered service.
For this reason, we have achieved to optimize the IPTV service by applying SDN solution in a MEC Architecture (Multiple-Access Edge Computing). In this way , through MEC Technology and SDN, it is possible to receive an IPTV service with Low Latency, High Performance and Low Bandwidth by solving successfully all the problems faced by the actual IPTV Operators. These improvements of delivering IPTV service through MEC will be demonstrated by using the OMNet +++ simulator in an LTE-A mobile network. The results show clearly that by applying the MEC technique in the LTE-A network for receiving IPTV Service through SDN Network, the service was delivered with latency decreased by >90% (compared to the cases when the MEC technique is not applied), with PacketLoss of almost 0 and with high performance QoE. In addition these strong Contributions, the ‘Big’ innovation achieved in this work through simulations is that the quality of delivered IPTV Service did not change according to the increasing of the end-users.This latency of delivering the video streaming services did not change. This means that the IPTV Service providers will increase their benefits by ensuring in the same time also the delivering of service with high quality and performance toward innumerous end users. Consequently, MEC Technology and SDN solution will be the two right and "smart" network choices that will boost the development of the 5th Mobile generation and will significantly improve the benefit of Video Streaming services offered by current providers worldwide (Netflix, HULU, Amazon Prime, YouTube, etc).
The document describes a multi-agent TV recommender system that uses three sources of user information - implicit viewing history, explicit preferences, and feedback on shows - to generate personalized program recommendations. It encapsulates this information into adaptive agents that collaborate to recommend shows. The system was tested on real users and found that the combination of implicit and explicit agents performed best.
How Open Data Can Enhance Interactive TelevisionLinkedTV
The presentation was delivered by Lyndon Nixon, STI International Consulting and Research GmbH, Austria, during the ngnlab.eu Workshop http://ngnlab.eu/index.php/ngnlabeu-workshop, held in Bratislava during September 20th, 2012. The workshop was co-located with the 5th joint IFIP Wireless and Mobile Networking Conference (WMNC 2012 http://wmnc.fiit.stuba.sk.
Purpose of the workshop is bringing together researchers and experts from academia as well as from business which came from Germany, Nederlands, Spain, Austria and Slovakia.
The document provides a summary of the LinkedTV project, which aims to seamlessly integrate television and web content. Key points:
- LinkedTV allows viewers to access background information, identify artists/museums from TV shows, and personalize the experience.
- It provides tools for automatic content analysis, enrichment with web data, an editor interface, and companion apps.
- A workflow enriches TV programs with metadata, stores it, and provides access via apps. Two apps were developed with broadcasters.
- The project concludes after 42 months, providing an end-to-end platform and tools to link TV and web content across devices.
Delay Efficient Method for Delivering IPTV ServicesIJERA Editor
Internet Protocol Television (IPTV) is a system through which Internet television services are delivered using
the architecture and networking methods of the Internet Protocol Suite over a packet-switched network
infrastructure, e.g., the Internet and broadband Internet access networks, instead of being delivered through
traditional radio frequency broadcast, satellite signal, and cable television (CATV) formats. IPTV provides
mainly three services: live TV, catch up TV, and video on demand (VoD).This paper focuses on delivering the
live TV services by exploiting the virtualised cloud architecture of the IPTV and statistical multiplexing. The
VoD tasks are prescheduled so that there will be less Instant Channel Change (ICC) delay. We select a proper
scheduling algorithm for rescheduling the VoD tasks. We then implement the scheduling algorithm for preshifting
the VoD tasks.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
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Topics covered:
• The role of a steering committee
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
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Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
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Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
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Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
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Power Grid Model
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Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
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In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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2. Which barriers stand in the way of AI adoption.
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6. Ideas and approaches to help build your organization's AI strategy.
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2. KIM et al.: AUTOMATIC RECOMMENDATION SCHEME OF TV PROGRAM CONTENTS FOR (IP)TV PERSONALIZATION 675
clustering are compared in this paper: demographic clustering more frequently watched TV program contents by an active user
and -means clustering. Finally, CF -based recommendation is are recommended in lower ranks. The proposed rank model in
performed with a novel ranking model which extends the Best this paper tries to remedy this weak point of the previous rank
Match (BM) model [7]–[9] to rank the candidate TV program models.
contents for recommendation. The proposed rank model is In the CF systems, for a large number of users, the process of
designed to make easy access to preferred TV program con- grouping similar users and recommending items entails a com-
tents. For the recommendation of popular or newly broadcast putational complexity issue [1], [3]. To solve the system over-
TV program contents, the popular TV program contents can load in clustering similar taste users for a large number of users,
be identified via CF from similar user groups. On the other G.R. Xue suggests a two-step clustering method by using of-
hand, newly broadcast TV program contents are preferably fline -means clustering and online PCC (Pearson correlation
recommended by restricting not recently broadcast TV program coefficient) clustering for the item ratings [16]. In this method,
contents outside a sliding time window in the history data of the -means clustering is performed offline for a large number
watched TV program contents. of users just one time for a given value. Then more similar
This paper is organized as follows: Section II reviews the pre- users are extracted online for active users based on PCC values
vious related works for recommendation; Section III introduces from their respective clusters to predict the rating values for the
the overall system architecture of our proposed automatic rec- unrated items. However, this method needs to know an appro-
ommendation scheme for TV program contents and describes priate cluster number a priori.
the data used for experiments; Section IV describes the compo- For user clustering in this paper, we compare two clustering
nents of the proposed recommendation scheme in detail—user methods: demographic clustering and -means clustering. The
profile reasoning, similar user clustering, and ranking of candi- demographic clustering is very simple to only use user’s demo-
date TV program contents for recommendation; In Section V, graphic information such as genders and ages for clustering. For
the proposed rank model is explained in detail; The experi- the -means clustering, we use the feature vectors of user pref-
mental results are presented in Section VI; Finally, Section VII erence values of 8 genres and 47 subgenres for TV program con-
concludes this work. tents. The former one is computationally very simple and can be
a solution for the cold-start problem which takes time to learn
II. RELATED WORKS users, but requires a priori knowledge about the demographic
PTV [4] adopted a hybrid method of CF and CBF to sup- information. On the other hand, the latter one does not require
plement the item ramp-up problem of CF and the user ramp-up such demographic information but implicitly clusters similar
problem of CBF [1]. It requires users to provide their preference users based on the genre preference from the watched history
information on contents while enrolling. Based on this prefer- data of TV program contents by users. For the -means clus-
ence information provided by the users, it creates and manages tering, an appropriate number for can be found by searching
user profiles with explicit ratings by users. However, in general a range based on dendrogram of hierarchical clustering [15],
users do not want to offer their personal information or some- [17], [18]. We then determine a value based on the smallest
times do not faithfully exhibit their interests on the items with sum of squared errors in this paper. The details of finding a right
explicit ratings. Pazzani et al. reported that only 15% of people value are described in Section IV. And as a rank model for
respond to the request for the relevance feedback on their pref- ordered recommendation of TV program contents, we propose
erence [5]. Therefore, requiring users to rate explicitly on items a novel rank model based on the BM model [7]–[9]. The pro-
is one of the main reasons that cause rating sparsity problems posed rank model is described in Section V in detail.
[3]. We can summarize the contribution points of our personal-
Deshpande M. et al. proposed an item based top- recom- ized automatic recommendation scheme for TV program con-
mendation algorithm [11] and J. Wang et al. [12] proposed an tents as follows: (1) it is more appropriate for TV program rec-
extension to “relevance model” from language model [13], both ommendation since it makes implicit reasoning for user prefer-
of which utilize CF with user-profile and item matching for ence on TV program contents from the watched TV program
item recommendation. The item based top- recommendation history data, which does not require users to explicitly rate their
algorithm uses an item-to-item matrix for item recommenda- watched TV program contents; (2) it takes into account not only
tion which is computed based on a user-to-item matrix [11] for the group preferences but also the individual user’s preferences
which the recommendation performance is further improved by on TV program contents for recommendation; and (3) the pro-
extending a language model to a relevance model [12]. posed rank model elaborates collaborative filtering by consid-
In item recommendation of e-commerce, recommender sys- ering the relative lengths of watching times for TV program con-
tems tend to suggest new items to the users because they are tents, not just by simply counting the number of users who have
not likely to repurchase the same items or similar kinds after watched them.
they have bought them. However, this may not be appropriate
in TV environments where TV viewers are expected to watch III. ARCHITECTURE OF THE PROPOSED RECOMMENDATION
(consume) the TV program contents (items) that they have been SYSTEM AND EXPERIMENT DATA
accustomed to watch. In general, TV viewers tend to watch pop-
ular TV program contents as their similar taste users do or spe- A. Proposed Recommendation System Scheme
cific TV program contents according to their individual pref- In this paper, it is assumed that TV terminals are connected
erences. So, the previous two models are insufficient in that the to the content servers of TV programs via back channels so that
3. 676 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 3, SEPTEMBER 2011
TABLE I
FIELDS OF TV USAGE HISTORY DATABASE
data set of 2,005 people is used which has been collected on
6 terrestrial TV channels for 6 months from Dec. 1, 2002 to
May 31, 2003. Table I shows the data fields of the usage history
data set for watched TV program contents. The TV program
contents in the history data set have 8 main genres which are
further divided into 47 subgenres in total. For the data set,
the total number of TV program titles amounts to 924 and the
total number of subtitles is 1,855. We use 795 TV program
Fig. 1. Architecture of the proposed recommendation system for TV program contents for training, corresponding to the first 4 months and
content. 629 TV program contents for testing, corresponding to the
last 2 months. Notice that the sum (1,424) of the TV program
contents for training and testing exceeds the total number (924)
the usage (or watching) history of (IP)TV program contents can of the titles because 500 watched TV program contents are
be collected at the server sides. In IPTV environments, TV pro- the titles that were repeatedly broadcast. Table I shows the
gram contents are streamed over IP networks and the respon- information attached to the broadcast TV program contents.
sible content providers at head-end sides can collect usage his-
tory of TV programs watched by the users via back channels. IV. PROPOSED RECOMMENDATION SCHEME
Fig. 1 shows the architecture of our proposed automatic recom-
mendation system for TV program contents. The automatic rec- A. User Profile Reasoning
ommendation scheme consists of three agents: (1) the user pro-
In this paper, a user is characterized in terms of his/her pro-
file reasoning agent computes user preferences on genres and
file which consists of two preferences on items (TV program
TV programs by analyzing user’s watching history of TV pro-
contents) and genres. First of all, we remove from the usage
gram contents. So, this agent collects TV usage history from
history data set all the TV program contents that have not been
local repositories of TV terminals for user profile reasoning; (2)
watched for more than 10% of their respective total lengths. The
the user clustering agent clusters the users (TV viewers) into
preference on a TV program content is defined as the relatively
similar preference user groups; (3) the recommendation agent
watched ratio over the total time length. For the reruns of TV
recommends to each active user a list of his/her preferred TV
program contents, they are all considered the same title (item).
program contents. Here, an active user means the user who logs
The preference on item by user is defined as
into the TV terminal and is ready to receive a recommended TV
program list.
For recommendation, a list of candidate TV program contents (1)
is extracted based on CF and our proposed rank model calculates
the respective scores of the candidate TV program contents for
where is the number of times being broadcast for an item .
ranked recommendation. Then the TV program contents with
And is defined as
the top highest scores are presented to the active user in a
descending rank order as the result of recommendation. Notice
in this paper that the users and items are interchangeably used (2)
with the TV viewers and TV program contents, respectively.
where and indicate the watched time length for item by
B. Description of Usage History Data Set for Watched TV
user and the total length of an item , respectively. It must be
Program Contents
pointed out in (1) that the item preference might be inaccurately
For the experiments to test the effectiveness of the proposed computed for inattentively watched TV program contents. The
recommendation scheme, Neilson Korea’s TV usage history treatment of them is out of scope in this paper.
4. KIM et al.: AUTOMATIC RECOMMENDATION SCHEME OF TV PROGRAM CONTENTS FOR (IP)TV PERSONALIZATION 677
Since the popularity or recency of TV program contents are TABLE II
often diminished with time and the user’ interest on TV pro- SELECTION RESULTS OF CLUSTER NUMBERS, K
gram contents varies over time, it is more appropriate to reflect
the recently watched TV program contents for recommendation.
Therefore, a time window function is defined as
(3)
where is a control parameter for the window size which is set and reveal the characteristics of cluster distributions, we take
to two-month or four-month length in this paper. a two-step approach: an unsupervised hierarchical clustering is
The average of user preference on item by user is given first run to construct a dendrogram for which a range of values
by is found by cutting its branches at the large jumps in a distance
criterion [14], [15], [17], [18]; the final value is then deter-
(4) mined in the range by repeatedly performing -means clus-
tering. To determine the final value, -means clustering is
repeated times for which the centroids of clusters are
where is the total number of items in the watched TV pro-
randomly initialized each time. When the clustering yields the
gram list by user .
same clustering results times for a given value, the clus-
In order to efficiently perform similar user clustering in low
tering results become the final clusters with the value. When
dimension, genre preference is used which can reflect the sim-
any does not result in the same clustering results less than
ilarity of user’s content consumption for TV program contents.
times, the value that results in the same clustering re-
Genre preference is computed by accumulating the item pref-
sults the largest times is selected as the final value. In this paper,
erence values for the genre and is then normalized by the total
and are set to 1,000 and 5, respectively. Table II
genre preference values for all genres. When the total number
shows ranges and finally selected values for the features
of genres is , the genre preference on genre by user
vectors of 47 subgenres and 8 main genres for the watched TV
is defined as
program contents by the users who have watched at least 33%
of the average number of watched TV program contents during
(5) the training period. For this experiment, the open-source code
Cluster 3.0 was used in [14].
The K-means clustering, which is the most time consuming
task in our scheme, takes less than one minute for
on a PC with Intel Core 2 Quad CPU 2.4 GHz and 2 GB
B. User Clustering memory.
For computational efficiency and effectiveness of collabora-
tive filtering, TV users are clustered into similar user groups. C. Recommendation of TV Program Contents
After clustering, each user has a membership to one of the user In order to recommend the preferred TV program contents
groups. Therefore, CF operation for an active user is performed to an active user, the recommendation process consists of three
for the user group to which the user belongs, not for the whole steps: extracting similar preference users from the clusters (sim-
users. ilar user groups) to which the active user belongs; selecting
For similar user grouping, two clustering approaches are candidate items for recommendation; and ranking the candidate
compared: demographic clustering based on genders and ages, items. Especially the rank model will be explained in Section V
and -means clustering based on the genre preference as in details.
described in (5). For -means clustering, two feature vectors 1) Selecting Similar Preference Users of an Active User in a
are compared with 8 preferences on the main genres and 47 Group: In Section IV-B, clustering the similar preference users
preferences on the subgenres, respectively. is done offline. For recommendation, more relevant users are
The demographic clustering is computationally very simple further extracted to construct a set of candidate TV program
but can only be used if the demographic information such as contents based on CF for the user group to which an active user
genders and ages is available. The demographic clustering can belongs. By doing so, the computation complexity is lowered by
avoid the cold-start clustering problem that usually takes time reducing the number of all users to the number of similar peer
while learning the users. In our demographic clustering, there users in the similar user group to which the active user belong.
are 26 combinations of different genders and ages. The genders Based on the proximity measure, the most peer
are divided into two classes—male and female and the ages are users with similar preference are extracted for the active user.
divided into 13 classes— , and For the proximity measure, the normalized correlation is
66 ages and higher. computed by subtracting the average preference value from all
As an alternative, -means clustering can be used, which the preference values [10].
does not require the demographic information. The essential On the basis of the consumed (watched) item (TV program
prerequisite for -means clustering is to know an appropriate contents) list of an active user , the similarity between
number as the number of clusters. In order to find a right and each peer user is measured as the proximity between
5. 678 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 3, SEPTEMBER 2011
the normalized preference values on items for and in the two conditions: one is that the weight is independent of term
similar preference groups. The similarity is defined as frequency; and the other is that the weight is linear with
term frequency. Each condition is satisfied as and
[9]. But, the second condition is not always satisfied
as . To remedy this, a scaling factor is added in
the numerator, thus resulting in .
This is taken into account in the BM11, 15 and 25 models [7].
(6)
The BM25 includes an inappropriate condition for TV pro-
where is an item belonging to . represents the active
gram recommendation since it gives a high weight on short doc-
user’s profile and indicates the preference value on item
uments compared to long documents by scope hypothesis [7].
consumed by user as in (1), and is the averaged item
Therefore, the proposed rank model in this paper extends the
preference value of user as in (4).
BM15 model which does incorporate the scope hypothesis into
The users with are only regarded as rel-
its rank model so that the TV program contents that were broad-
evant users to the active user. Then CF is performed for the
cast less times are prevented from being higher-ranked.
item lists between the active user and each of the relevant users.
Since the number of similar preference users affects precision B. Proposed Rank Model
performance, we need to find an optimal number of peer users
based on the average precision accuracy, which is explained in An extension to the BM15 is made by taking into account
Section VI. the collaborative filtering concept that accounts for the watching
2) Filtering Candidate Items With EPG Information: After times of users in the rank model for recommendation of TV pro-
selecting the relevant users for an active user, their preference gram contents. Furthermore, we add to the rank model a weight
items become the candidate items for recommendation. But with the correlation between candidate items for recommenda-
some items may not be available in TV channels due to the tion and the items watched by the active user. We score the fil-
termination of broadcasting for the TV program contents. In tered candidates of TV program contents in a ranked order. The
case of linear TV broadcasting services, Electronic Program relations between candidate document and query in BM15
Guide (EPG) information can be used to filter out the candidate are translated into the relations between candidate TV program
TV program contents which are not available. contents for recommendation and the active user .
3) Ranking Items: After a set of candidate TV program con- To make the BM15 be applicable for recommendation of TV
tents for recommendation is determined, they are ordered by a program contents, we have the following assumptions: (1) the
rank model. Finally, the recommended TV program contents are watched TV program list represents the active user ; (2)
presented to active users in the descending order of rank scores. is transformed into the relative watching frequencies of both
The proposed rank model is described in the following section. TV program contents of similar preference users and
of an active user by applying CF concept, where
indicates the watched TV program contents by ; (3)
V. PROPOSED RANK MODEL
is regarded as the relative watching frequency of by ;
A. Related Work—BM Model (4) the similarity between and by is further taken into
account. The matching score between and is defined as
Our proposed ranked model extends the Best Match (BM) our proposed rank model by
model [7]–[9]. The BM is a ranking function used by retrieval
engines to rank matching documents according to their rele-
vance to a given query. The BM model is given by
(7)
(9)
where where and are used to balance the term frequency and
the query term frequency in the rank model. The Robertson
(8) et al. analyzed the way of weighting in details [7]. and
are set to 200 and 0.2 empirically in this paper. In (9),
indicates the relative watching frequency
In (8), is the number of total documents, is the number of
which is the ratio of the total number of watching times of both
documents including a specific term of query, is the number of
programs and over the total number of watching times of
documents related with a specific topic, and is the number of
the TV program contents (all ’s) by the peer users.
documents including a specific term of query and is related with
Therefore, the relative watching frequency is calculated as
the specific topic [8]. In (7), is term frequency in documents
and is term frequency in the query.
The BM model originates from two Poisson models that
the term frequency is independent of relevant and irrelevant (10)
documents [9]. Based on this idea, the simple formation
is suggested under the following
6. KIM et al.: AUTOMATIC RECOMMENDATION SCHEME OF TV PROGRAM CONTENTS FOR (IP)TV PERSONALIZATION 679
items. There are two users, and , who have watched two
items (TV program contents) and , and the similarity (VCC)
value between and is 0.643. On the contrary, for the two
users ( and ) who have watched both and , the VCC
value between and is 0.4. So, if we set 0.5 of the VCC value
as a threshold for the similarity between items, then the items
and are considered being “similar”, but and are not sim-
ilar. So, in (9) can improve the rank model by taking into
account the relation between the active user and the candidate
items for the score calculation. The effect of on precision
performance will be shown in Fig. 5 in Section VI.
VI. EXPERIMENTAL RESULTS
For the usage history of watched TV program contents ex-
plained in Section III-B, we use the usage history data of four
months for training and the remaining two months for testing.
Fig. 2. Illustration for significance on weights w . In this experiment section, we measure the performance of
our recommendation scheme in terms of both precision/recall
and Average Normalized Modified Retrieval Rank (ANMRR)
In (9), indicates the ratio of the total number of which considers the rank orders in retrieval [19]–[21].
watching times of TV program contents over the total number
of watching times for all the TV program contents (all ’s) by A. Performance Measure of Rank Models
, and is given by
Precision and Recall: The performance in information re-
(11) trieval is usually measured in terms of precision and recall [22].
The precision is defined as the ratio of how many watched TV
program contents (relevant documents) are contained in the rec-
ommendation list (retrieved documents) of TV program con-
Eq. (9) can be explained intuitively as follow: tents for an active user. The recall is defined as the ratio of
is regarded as the peer users’ preferences on TV program how many recommended TV program contents (retrieved doc-
contents in the same user group to which belongs; and uments) are actually included in the watched TV programs (rel-
is referred to as the active user’s preference on evant documents) for the active user. The precision and recall
TV program contents. The two terms and are defined as.
are in mutually supplemental relation as being
multiplied together. (14)
In (9), . Two weights and are given
as
(15)
(12)
where is the number of watched TV pro-
gram contents in the recommended list of TV program contents
(13) and is the number of recommended TV program con-
tents. is the number of recommended TV
In (12), indicates the total number of broadcast times for all program contents in the watched list of TV program contents,
items and is the number of broadcast times of each item. and is the number of watched TV program contents.
reflects the inverse document frequency with independence For the recommendation of TV program contents, the precision
assumption between the documents with and without the terms is a more appropriate metric for performance evaluation than the
[8]. In this paper, it is assumed that the document for retrieval recall since the recall accuracy is increased as the number of rec-
is and the specific term of query is from active user profile ommended TV program contents increases. In this regard, rec-
. is added as a weight for the similarity between ommending a larger number of TV program contents increases
and which is calculated as vector cosine correlation simi- false positives. So, in this paper, we use precision accuracy for
larity in (13) for which the and are the feature performance evaluation. However, if the number of ground truth
vectors of user preference on program and , respectively. increases, the precision also becomes higher. So, the perfor-
This weight puts more emphasis on the active user’s personal mance of the rank models is measured in terms of precision and
preference on TV program contents, which is not reflected in recall.
the original BM [7], [8]. ANMRR: Compared to precision measure, another perfor-
In order to see the effectiveness of in (13), Fig. 2 il- mance measure, ANMRR [19]–[21], is considered which has
lustrates an example of similarity measures between two been developed to measure the image retrieval performance in
7. 680 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 3, SEPTEMBER 2011
Fig. 3. Preferences on genres and channels for groups: Demographic Clustering (DM) vs. K -means clustering (KM). (a) Genre preferences of groups. (b) Channel
preferences of groups.
MPEG-7 [20]. The ANMRR indicates not only how many cor- A cluster ’s preference on a specific genre is computed by
rect items are recommended but also how highly more relevant accumulating the preferences on the specific genre by all users
items are ranked among the recommended items. For ANMRR, in the same cluster, and then it is normalized by the total number
Normalized Modified Retrieval Rank (NMRR) is defined as of users in the cluster . Similarly, the normalized pref-
erence on a channel can also be computed for each cluster. The
preferences and on genre and channel for a cluster
are calculated as
(16)
where is the number of recommended TV program con- (19)
tents that the active user has really watched longer than the av-
erage watching times of his/her preferred TV program contents
during test period. is the allowable maximum rank and (20)
is computed as where
is the maximum of [21]. And the in
(16) is revised by where is the total number of users in the cluster . and
are the total numbers of genres and channels, respectively.
(17) Fig. 3 shows the profiles of clusters’ preferences on genres
and channels of TV programs. As shown in Fig. 3, the genre
preferences are not significantly distinguished among different
where is the rank ordered in score values by the pro- groups by demographic clustering (DM). On the other hand, the
posed rank model in this paper. Finally ANMRR is written as groups by -means clustering (KM) show somewhat different
follows: patterns for genre preferences among them. This is also sim-
ilarly observed for the channel preferences except the group4
(18) and group5 by DM.
Table III shows the average precision performance for dif-
ferent numbers of groups by DM and KM. Although the pref-
erences on genres and channels are better distinguished by KM
B. Clustered Data Analysis
than DM for different groups, the performance difference of av-
As explained before, two clustering methods are compared erage precision between DM and KM is very slight. In this ex-
between the demographic clustering and -means clustering. periment, the KM turns out to be effective for recommendation
8. KIM et al.: AUTOMATIC RECOMMENDATION SCHEME OF TV PROGRAM CONTENTS FOR (IP)TV PERSONALIZATION 681
TABLE III TABLE V
COMPARISONS OF AVERAGE PRECISION BETWEEN DM AND KM PRECISION ACCURACY WITH OUTLIER REMOVALS
outlier criteria (33%); refer to Table V.
the number of peer user is 5; refer to Fig. 8. the number of cluster is 26 by DM; refer to Table IV.
the number of peer user is 5; refer to Fig. 8.
TABLE IV
AVERAGE PRECISION PERFORMANCE FOR THE NUMBER OF CLUSTERS
outlier criteria (33%); refer to Table V.
the number of peer user is 5; refer to Fig. 8.
Fig. 5. Performance comparison with/without w .
o’clock” and “Let’s marry”. For both TV program contents,
there are relatively large numbers of users who have watched
them less than 10% or more than 95% of the total TV program
lengths, respectively. This pattern is similarly observed in other
TV program contents. So, we set 10% of the total length of TV
program contents as a threshold for outlier removal.
Table V shows the average precision performance on different
thresholds of outlier removal for the second case. With the ex-
clusion of users who watched the TV program content less than
the 33% of the average number of watched TV program con-
Fig. 4. Number of users versus relative watching lengths of TV program
contents. tents, we obtain 76.6% of average precision accuracy for the
Top-5 recommendation. The threshold values in Table V indi-
cate the ratios of the number of watched TV program contents
although though it does not utilize the demographic information by each user over the average number of watched TV program
for clustering. contents by all users during the training period of 4 months. The
Table IV shows the average precision performance on Top- average numbers of watched TV program contents by all users
recommendations for different numbers of clusters (groups) by are 124 and 90 during the training period of 4 months and the
DM. Increasing the number of clusters does not enhance the testing period of 2 months, respectively.
precision performance because we only use the most similar
users to an active user of his/her group (The average D. Effect of on Precision Performance of the Proposed
precision performance according to the number of peer users Rank Model
will be shown in Fig. 8). Fig. 5 shows the performance comparison in terms of average
precision accuracy for the recommended TV program contents
C. Exclusion of Nosy Items and Outliers of Users with and without in (9).
For the experiments, two kinds of outliers are removed to have The average precision accuracies with in the proposed
reliable recommendation: firstly, the TV program contents that rank model are higher than those without it. The average preci-
were watched less than 10 % of their respective whole lengths sion in this experiment is measured with 67 active users.
are removed as noise; secondly, the users who have watched TV
program contents less than a predefined TV watching times are E. Performance Comparison Between Proposed Rank Model
excluded. and Linear Model
Fig. 4 shows the number of users versus the relative watching For performance comparison in precision and ANMRR be-
lengths for the two TV program contents—“Hometown at 6 tween the proposed rank model and the linear model [12], 90
9. 682 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 3, SEPTEMBER 2011
Fig. 6. Performance comparison in precision and recall.
Fig. 8. Precision accuracies versus different numbers of similar peer users.
(a) Precision accuracies with Top-5 recommendations. (b) Precision accuracies
with four clusters.
Fig. 7. Performance comparison in ANMRR.
3) It takes into account the number of watching times for both
TV program contents and by peer users in the score
users are randomly selected. Figs. 6 and 7 show the perfor- calculation for ranking. This more elaborates collaborative
mance comparisons of the proposed model and linear model in filtering. On the other hand, the linear rank model [12]
precision-recall and ANMRR, respectively. The proposed rank simply counts the number of peer users who have watched
model outperforms the linear model in both precision-recall and both and .
ANMR.
Notice that the smaller the ANMRR value is, the better F. Performance Analysis for Proposed Recommendation
the recommendation performance is. Ideally, the case of Scheme
is achieved when the ranked order of the rec- We investigate the performance of the proposed recommen-
ommended TV program contents is perfectly matched with dation in terms of the number of clusters, the number of sim-
the order of the watched TV program contents by the active ilar peer users and the number of TV program contents for final
user during the test period. Therefore the recommended TV recommendation. Fig. 8 shows precision performance for dif-
program contents by the proposed rank model are also better ferent numbers of similar peer users given Top-5 recommenda-
matched in ranked orders than the linear model. tions and 4 clusters.
The superiority of our proposed rank model comes from the In Fig. 8(a), the average precision performance slightly de-
facts that: creases as the number of similar peer users increases for dif-
1) The proposed rank model defines the weight in (12) ferent numbers of clusters. This is because a smaller number
such that more frequently broadcast TV program contents of similar peer users yields more correlation between the ac-
are put in lower ranks; tive user and peer users so that the resulting recommendation
2) For recommendation of TV program contents, the tradi- precision is usually enhanced. When the number of clusters in-
tional models usually intensify the preference of the peer creases, the resulting recommendation precision seldom varies
users but relatively reduce the preference of an active user, for different numbers of recommended items, because the larger
which might be appropriate to recommend unpurchased the number of clusters, the more correlate the clustered users
items to active users in e-commerce environments. How- are. In Fig. 8(b), the average precision performance of the pro-
ever, in TV environments, users often tend to watch the posed recommendation scheme becomes lowered as the number
TV program contents that they used to watch. Therefore, it of recommended TV program contents increases.
is reasonable to take into consideration the preferences of Table VI shows the 19 recommended TV program contents
both similar users and an active user for recommendation. by the proposed rank model for the corresponding ground truth
The proposed rank model actually considers both; items out of 67 for an active user with . As
10. KIM et al.: AUTOMATIC RECOMMENDATION SCHEME OF TV PROGRAM CONTENTS FOR (IP)TV PERSONALIZATION 683
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the usage history data of watched TV program contents. The
rank model in the proposed scheme takes into account not only
the group preferences but also the active user’s preferences on EunHui Kim received the B.E. degree in infor-
TV program contents. Furthermore, the proposed rank model mation and communications engineering from
elaborates collaborative filtering by considering the relative Chungnam National University in 2000 and the
M.Sc. degree in information communications engi-
lengths of watching times for TV program contents, not just by neering from Korea Advanced Institute and Science
simply counting the number of users who have watched them. Technology (KAIST), Daejeon, Korea in 2009. She
Our proposed recommendation scheme shows the effectiveness is currently pursuing the Ph.D. degree in Department
of Electrical Engineering at KAIST.
with rich experimental results for a real usage history dataset She worked for Samsung Electronics as an As-
of watched TV program contents. sistant Engineer of Software team in Visual Display
Division during 2000-2003 in Suwon, Korea and as
REFERENCES an Associate Engineer of Architecture team in Digital Solution Center during
2003–2007 in Seoul Korea. Her research interests include personalization in
[1] M. Montaner, B. Lopez, and J. L. DE Larosa, “A taxonomy of recom- connected TV, data clustering, collaborative filtering, and recommendation
mender agents on the Internet,” AI Rev., vol. 19, pp. 285–330, 2003. modeling with AI for smart TV interaction.
11. 684 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 3, SEPTEMBER 2011
Shinjee Pyo received the B.E. degree and the Munchurl Kim (M’07) received the B.E. degree in
M.Sc. degree in information and communications electronics from Kyungpook National University,
engineering from KAIST, Daejeon, Korea, in 2007 Korea in 1989, and the M.E. and Ph.D. degrees in
and 2009, respectively. She is currently pursuing electrical and computer engineering from University
the Ph.D. degree in information and communica- of Florida, Gainesville, Florida, in 1992 and 1996,
tions engineering at KAIST. Her research interests respectively.
include Personalization in Connected TV, sequential After his graduation, he joined Electronics and
pattern mining for TV personalization and pattern Telecommunications Research Institute (ETRI)
recognition. where he had led Broadcasting Media Research
Team and Realistic Broadcasting Research Team,
and had worked in the MPEG-4/7 standardization
related research areas. In 2001, he joined, as Assistant Professor in School of
Engineering, the Information and Communications University (ICU) in Taejon,
Korea. Since 2009, he is Associate Professor in Department of Electrical
Eunkyung Park received the B.E. degree in in- Engineering at KAIST, Daejeon, Korea. His research areas of interest include
formation and communications engineering and 2D/3D video coding, 3D video quality assessment, pattern recognition and
the M.Sc. in electrical engineering from KAIST, machine learning, and video analysis and understanding.
Deajeon, Korea, in 2009 and 2011, respectively.
Now she joins NAVER which is the first and largest
search portal in Korea and is working with business
and planning for web portal services. Her research in-
terest is statistical learning theory, social networking,
and machine learning.