This document discusses an approach for automatically detecting communities on the collaborative web based on users' resource manipulations. It presents a fuzzy K-means clustering method that groups users into communities based on the similarity of resources they interact with. It also describes how new resources can be dynamically tagged based on which communities interact with them. The approach was tested on movie rating datasets to automatically detect tags for new movies based on the communities of users that rated them.
This document discusses a Bayesian approach to active learning for collaborative filtering. It summarizes that collaborative filtering uses preference patterns to predict user ratings, but requires many user ratings for accuracy. Active learning aims to acquire the most informative ratings from users. Previous active learning methods only consider estimated models, which can be misleading with few ratings. The proposed method takes a full Bayesian approach, averaging expected loss over the posterior model distribution to account for model uncertainty and avoid problems from estimated models. It aims to select items that maximize reduction in expected loss when considering the full model distribution, rather than just an estimated model.
This document summarizes a research paper that proposes a social ranking technique using tag-based recommender systems to uncover relevant content from large datasets. It outlines the problem of content overload and sparse tagging in social bookmarking sites. The researchers analyzed a dataset from CiteULike, identifying properties of users' tagging behavior. They developed a social ranking query model that expands queries based on tag similarity to improve accuracy and coverage compared to standard information retrieval systems. The model is evaluated and compared to related work.
AN GROUP BEHAVIOR MOBILITY MODEL FOR OPPORTUNISTIC NETWORKS csandit
Mobility is regarded as a network transport mechanism for distributing data in many networks.
However, many mobility models ignore the fact that peer nodes often carried by people and
thus move in group pattern according to some kind of social relation. In this paper, we propose
one mobility model based on group behavior character which derives from real movement
scenario in daily life. This paper also gives the character analysis of this mobility model and
compares with the classic Random Waypoint Mobility model.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
User Behaviour Modeling on Financial Message Boardsprithan
Online social communities like discussion boards and message boards are fast evolving in
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they connect people (most often with no offline links) from different backgrounds and histories.
Various theories exist in sociology about the intended behavior of users in online forums. In this
paper, we study the applicability of one such theory - “Participation Inequality” on financial
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content of postings and employ Machine Learning techniques to identify, cluster and infer roles
of users exhibiting similar behavior.
User Identity Linkage: Data Collection, DataSet Biases, Method, Control and A...IIIT Hyderabad
Online Social Networks (OSNs) are popular platforms for online users. Users typically register and maintain their accounts (user identities) across different OSNs to share a variety of content and remain connected with their friends. Consequently, linking user identities across OSN platforms, referred to as user identity linkage (UIL) becomes a critical problem. Solving this problem enables us to build a more comprehensive view of user’s activities across OSNs, which is highly beneficial for targeted advertisements, recommendations, and many more applications. In the thesis, we propose approaches for analyzing data collection methods, investigating biases in identity linkage datasets, linkage of user identities across social networks, control-ability of user identity linkage, and application of user identity linkage solutions to solve related problems.
A bluetooth-low-energy-dataset-for-the-analysis-of-social-inte 2020-data-in-Tony Vilchez Yarihuaman
This dataset contains Bluetooth Low Energy (BLE) beacon data collected from experiments involving human social interactions. Volunteers wore BLE tags that emitted beacons, and a mobile app collected beacons from other devices. There were 6 experimental sessions with different interaction conditions involving posture, device position, group size, and interaction stages (non-interaction, approaching, interaction). Each session contained multiple tests that were repeated. The raw BLE beacon data is provided in CSV files along with a ground truth annotation of interaction times. The dataset is intended to help analyze social interactions and detect the different stages through algorithms using the BLE beacon data and ground truth.
This document discusses a Bayesian approach to active learning for collaborative filtering. It summarizes that collaborative filtering uses preference patterns to predict user ratings, but requires many user ratings for accuracy. Active learning aims to acquire the most informative ratings from users. Previous active learning methods only consider estimated models, which can be misleading with few ratings. The proposed method takes a full Bayesian approach, averaging expected loss over the posterior model distribution to account for model uncertainty and avoid problems from estimated models. It aims to select items that maximize reduction in expected loss when considering the full model distribution, rather than just an estimated model.
This document summarizes a research paper that proposes a social ranking technique using tag-based recommender systems to uncover relevant content from large datasets. It outlines the problem of content overload and sparse tagging in social bookmarking sites. The researchers analyzed a dataset from CiteULike, identifying properties of users' tagging behavior. They developed a social ranking query model that expands queries based on tag similarity to improve accuracy and coverage compared to standard information retrieval systems. The model is evaluated and compared to related work.
AN GROUP BEHAVIOR MOBILITY MODEL FOR OPPORTUNISTIC NETWORKS csandit
Mobility is regarded as a network transport mechanism for distributing data in many networks.
However, many mobility models ignore the fact that peer nodes often carried by people and
thus move in group pattern according to some kind of social relation. In this paper, we propose
one mobility model based on group behavior character which derives from real movement
scenario in daily life. This paper also gives the character analysis of this mobility model and
compares with the classic Random Waypoint Mobility model.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
User Behaviour Modeling on Financial Message Boardsprithan
Online social communities like discussion boards and message boards are fast evolving in
their usage bringing people with similar interests together. From a social and anthropological
standpoint, these are the most interesting to study compared to Online Social Networks because
they connect people (most often with no offline links) from different backgrounds and histories.
Various theories exist in sociology about the intended behavior of users in online forums. In this
paper, we study the applicability of one such theory - “Participation Inequality” on financial
message boards. We consider the activity of user, his network interaction structure and the
content of postings and employ Machine Learning techniques to identify, cluster and infer roles
of users exhibiting similar behavior.
User Identity Linkage: Data Collection, DataSet Biases, Method, Control and A...IIIT Hyderabad
Online Social Networks (OSNs) are popular platforms for online users. Users typically register and maintain their accounts (user identities) across different OSNs to share a variety of content and remain connected with their friends. Consequently, linking user identities across OSN platforms, referred to as user identity linkage (UIL) becomes a critical problem. Solving this problem enables us to build a more comprehensive view of user’s activities across OSNs, which is highly beneficial for targeted advertisements, recommendations, and many more applications. In the thesis, we propose approaches for analyzing data collection methods, investigating biases in identity linkage datasets, linkage of user identities across social networks, control-ability of user identity linkage, and application of user identity linkage solutions to solve related problems.
A bluetooth-low-energy-dataset-for-the-analysis-of-social-inte 2020-data-in-Tony Vilchez Yarihuaman
This dataset contains Bluetooth Low Energy (BLE) beacon data collected from experiments involving human social interactions. Volunteers wore BLE tags that emitted beacons, and a mobile app collected beacons from other devices. There were 6 experimental sessions with different interaction conditions involving posture, device position, group size, and interaction stages (non-interaction, approaching, interaction). Each session contained multiple tests that were repeated. The raw BLE beacon data is provided in CSV files along with a ground truth annotation of interaction times. The dataset is intended to help analyze social interactions and detect the different stages through algorithms using the BLE beacon data and ground truth.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
1) The document proposes an enhanced technique to recommend communities to users in social networks based on the user's interests and their strong friends.
2) It identifies a user's area of interest by analyzing their posts and classifying keywords. It then determines the user's strong friends based on an enhanced quasi-clique technique, considering interaction strength.
3) Communities are recommended by considering both the user's interests and strong friends. This provides a more precise recommendation than only considering strong friends.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
This document summarizes a research paper that proposes a MapReduce algorithm called 3MA for scalable local community detection in large networks. 3MA parallelizes an existing iterative expansion algorithm that uses the M metric to evaluate communities. It distributes the computation of node degrees and community M measures across multiple systems. Experimental results showed 3MA can detect communities in networks with millions of nodes faster than sequential algorithms.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
This document summarizes a research paper that proposes a novel approach to discovering user interests on e-commerce websites based on their clickstream data. The approach involves developing a rough leader clustering algorithm using indicators like category visiting paths, visiting frequencies, and durations to measure user similarities and group users into clusters with similar interests. The algorithm starts with a random leader and assigns other users to clusters based on similarity thresholds. It allows users to belong to multiple clusters to account for overlapping interests.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses community detection and behavior prediction in social networks using data mining techniques. It introduces key concepts in social media and networking, outlines common data mining tasks like community detection and centrality analysis, and evaluates different methods. Community detection aims to identify tightly knit groups within networks, while behavior prediction uses network structure and attributes to predict node characteristics. The document also discusses data visualization and modeling of social networks.
The document presents a new greedy incremental approach for community detection in social networks. It begins by calculating the degree of nodes and sorting them in descending order. Initial communities are formed with the highest degree nodes. Then nodes are incrementally added to communities if it increases the community density. The approach is tested on standard datasets and able to detect communities reasonably well in less dense graphs. However, there is scope to improve performance on very dense graphs such as implementing it in parallel processing.
Graph Based User Interest Modeling in Twitterraghavr186
This document summarizes a research project on modeling user interests on Twitter using a graph-based approach. The project aims to predict a user's interest profile based on the interests of the users they follow on Twitter. Various graph features are explored as weighting schemes to calculate the influence of followers on a user's interests. Experimental results show that features based on retweets and mentions perform best at predicting interests, with F1 scores around 0.6. A composite model is also proposed that combines predictions from different weighting schemes using learned quality scores. Additionally, a machine learning model is trained to predict interests directly from graph features.
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dyna...Subhajit Sahu
Highlighted notes during research with Prof. Dip Sankar Banerjee, Prof. Kishore Kothapalli:
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs.
https://ieeexplore.ieee.org/document/9384277
There are 3 types of community detection methods:
Divisive, Agglomerative, and Multi-level (usually better).
In this paper, heuristics for skipping out most likely unaffected vertices for a modularity-based community detection method like Louvain and SLM (Smart Local Moving) is given. All edge batches are undirected, and sorted by source vertex id. For edge additions, source vertex i, highest modularity changing edge vertex j*, i's neighbors, and j*'s community are marked as affected. For edge deletions, where i and j must be in the same community, i, j, i's neighbors, and i's community are marked as affected. Performance is compared with static, dynamic baseline (incremental), and this method (both Louvain and SLM). Comparison is also done with "DynaMo" and "Batch" community detection methods.
The EigenRumor algorithm calculates contribution scores for participants and information objects in online communities. It considers information provision and evaluation as links between participants and objects. The algorithm calculates three mutually reinforcing scores: authority score for participants' information provision ability, hub score for their evaluation ability, and reputation score for objects. The reputation score of an object is influenced by the authority score of its provider and hub scores of evaluators. In turn, authority and hub scores are influenced by the reputation scores of objects participants provide or evaluate. Calculating the scores through this mutually reinforcing process allows the algorithm to identify high contributors.
Social media recommendation based on people and tags (final)es712
1) The document proposes methods to generate personalized recommendations in social media platforms based on people relationships and tags.
2) An evaluation of three recommendation approaches that utilize direct tags, indirect tags through related items, and incoming tags from other users found that a combination of direct tags and incoming tags most accurately represented a user's interests.
3) A user study tested five recommendation approaches and found that combining people relationships and tags into a user profile achieved the highest ratings for interesting recommendations and lowest for non-interesting items.
Towards a hybrid recommendation approach using a community detection and eval...IJECEIAES
In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy.
Measuring the Topical Specificity of Online CommunitiesMatthew Rowe
This document proposes and evaluates a method for measuring the topical specificity of online communities. It begins by explaining why measuring specificity is important for tasks like tracking community focus and recommending new communities to users. It then presents an approach that derives a concept model for each community from post content, selects concepts using composite functions, and measures concept abstraction using information theoretic metrics. Five abstraction measures are described, including network entropy, centrality, statistical subsumption, and key player problem. The approach is evaluated by comparing automatically generated specificity rankings to ground truth ranks.
Control of Photo Sharing on Online Social Network.SAFAD ISMAIL
A social networking service (also social networking site', SNS or social media) is an online platform which people use to build social networks or social relations with other people who share similar personal or career interests, activities, backgrounds or real-life connections.
Identifying ghost users using social media metadata - University College LondonGreg Kawere
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information a joint research project of the Alan Turing Institute and University College in London
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.Title: MUTATION AND CROSSOVER ISSUES FOR OSN PRIVACY
Author: C. Narasimham, Jacob
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350-1022
Paper Publications
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Novel R&D Capabilities as a Response to ESG Risks-Lessons From Amazon’s Fusio...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
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Community detection from complex information networks draws much attention from both academia and
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This document summarizes a research paper that proposes a novel approach to discovering user interests on e-commerce websites based on their clickstream data. The approach involves developing a rough leader clustering algorithm using indicators like category visiting paths, visiting frequencies, and durations to measure user similarities and group users into clusters with similar interests. The algorithm starts with a random leader and assigns other users to clusters based on similarity thresholds. It allows users to belong to multiple clusters to account for overlapping interests.
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses community detection and behavior prediction in social networks using data mining techniques. It introduces key concepts in social media and networking, outlines common data mining tasks like community detection and centrality analysis, and evaluates different methods. Community detection aims to identify tightly knit groups within networks, while behavior prediction uses network structure and attributes to predict node characteristics. The document also discusses data visualization and modeling of social networks.
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https://ieeexplore.ieee.org/document/9384277
There are 3 types of community detection methods:
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The EigenRumor algorithm calculates contribution scores for participants and information objects in online communities. It considers information provision and evaluation as links between participants and objects. The algorithm calculates three mutually reinforcing scores: authority score for participants' information provision ability, hub score for their evaluation ability, and reputation score for objects. The reputation score of an object is influenced by the authority score of its provider and hub scores of evaluators. In turn, authority and hub scores are influenced by the reputation scores of objects participants provide or evaluate. Calculating the scores through this mutually reinforcing process allows the algorithm to identify high contributors.
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This document proposes and evaluates a method for measuring the topical specificity of online communities. It begins by explaining why measuring specificity is important for tasks like tracking community focus and recommending new communities to users. It then presents an approach that derives a concept model for each community from post content, selects concepts using composite functions, and measures concept abstraction using information theoretic metrics. Five abstraction measures are described, including network entropy, centrality, statistical subsumption, and key player problem. The approach is evaluated by comparing automatically generated specificity rankings to ground truth ranks.
Control of Photo Sharing on Online Social Network.SAFAD ISMAIL
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Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.Title: MUTATION AND CROSSOVER ISSUES FOR OSN PRIVACY
Author: C. Narasimham, Jacob
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ISSN: 2350-1022
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Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
NOVEL R & D CAPABILITIES AS A RESPONSE TO ESG RISKS- LESSONS FROM AMAZON’S FU...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
Building on this premise, this paper conducts an empirical analysis, utilizing reliable firms data on ESG
risk and brand value, with a focus on 100 global R&D leader firms. It analyzes R&D and actions for ESG
risk mitigation, and assesses the development of new functions that fulfill F + ESG optimization through
R&D. The analysis also highlights the significance of network externality effects, with a specific focus on
Amazon, a leading R&D company, providing insights into the direction for transforming R&D strategies
towards F + ESG optimization.
The dynamics of stakeholder engagement in F + ESG optimization are indicated with the example of
amazon's activities. Through the analysis, it became evident that Amazon's capacity encompassing growth
and scalability, specifically its ability to grow and expand, is accelerating high-level research and
development by gaining the trust of stakeholders in the "synergy through R&D-driven ESG risk
mitigation."
Finally, as examples of these initiatives, the paper discussed the Climate Pledge led by Amazon and the
transformation of Japan's management system.
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these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
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Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
MEDIATING AND MODERATING FACTORS AFFECTING READINESS TO IOT APPLICATIONS: THE...IJMIT JOURNAL
Although IOT seems to be the upcoming trend, it is still in its infancy; especially in the banking industry.
There is a clear gap in literature, as only few studies identify factors affecting readiness to IOT
applications in banks in general, and almost negligible investigations on mediating and moderating
factors. Accordingly, this research aims to investigate the main factors that affect employees’ readiness to
IOT applications, while highlighting the mediating and moderating factors in the Egyptian banking sector.
The importance of Egypt stems from its high population and steady steps taken towards technology
adoption. 479 valid questionnaires were distributed over HR employees in banks. Data collected was
statistically analysed using Regression and SEM. Results showed a significant impact of ‘Security’,
‘Networking’, ‘Software Development’ and ‘Regulations’ on ‘readiness to IOT applications. Thus, the
readiness acceptance level is high‘Security’ and ‘User Intention’ were proven to mediate the relationship
between research variables and readiness to IOT applications, and only a partial moderation role was
proven for ‘Efficiency’. The study contributes to increasing literature on IOT applications in general, and
fills a gap on the Egyptian banking context in particular. Finally, it provides decision makers at banks with
useful guidelines on how to optimally promote IOT applications among employees.
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IT (Information and Communication Technology) companies are facing the dilemma of decreasing
productivity despite increasing research and development efforts. M&A (Merger and Acquisition) is being
considered as a breakthrough solution. From existing research, it has been pointed out that M&A leads to
the emergence of new innovations. Purpose of this study was to discuss the efficient ways of acquisition and
to resolve the dilemma of productivity decline by clarifying how the technology obtained through M&A
leads to the creation of new innovations. Hypothesis 1 was that the technology acquired through M&A is
utilized for innovation creation, Hypothesis 2 was that the acquired technology is utilized over a long
period of time, and Hypothesis 3 was that a long-term utilization has a positive impact on corporate
performance. The results, using sports prosthetics as a case study and using patents as a proxy variable,
confirmed all the hypotheses set. We have revealed that long-term utilization of technology obtained
through M&A is effective for creating new innovations.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly peer-reviewed journal that publishes articles on the strategic application of information technology in organizations from both academic and industry perspectives. The journal focuses on innovative uses of IT to support organizational goals and foster collaboration both within and outside organizations. It covers topics such as education technology, e-government, healthcare IT, mobile systems, and more. Authors are invited to submit original research papers for consideration through the journal's online submission system.
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023)IJMIT JOURNAL
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Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
TRANSFORMING SERVICE OPERATIONS WITH AI: A CASE FOR BUSINESS VALUEIJMIT JOURNAL
This document discusses how AI-powered service operations (AIServiceOps) can create business value through digital transformation. It begins with background on digital transformation and how AI is driving changes in IT service operations. It then examines how AIServiceOps can streamline processes, provide insights, and improve customer experience. A case study is presented showing how one company implemented AIServiceOps to significantly reduce response times, increase customer satisfaction, and lower costs. The document argues that AIServiceOps can deliver both quantifiable and flexible benefits while enhancing organizational resilience and sustainability over the long term.
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research framework using survey data from 210 respondents. The collected data has been analyzed using
Smart PLS software. The results of the study show that trust, self-motivation, and altruism are positively
related to attitude. Contrary to our expectations, knowledge technology negatively affects attitude.
However, reward systems and empowerment by leaders are significantly associated with knowledgesharing intentions.Knowledge-sharing intention, in turn, was significantly related to digital knowledgesharing behavior. The contributions of this study are twofold. The framework may serve as a roadmap for
future researchers and managers considering their strategy to enhance digital knowledge sharing in HEI.
The findings will benefit academic staff and university administrations.The study will also help academic
staff enhance their knowledge-sharing practices.
BUILDING RELIABLE CLOUD SYSTEMS THROUGH CHAOS ENGINEERINGIJMIT JOURNAL
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given point in time. The complex nature of cloud systems is the motivation to conduct research in novel
ways of ensuring that cloud systems are built with reliability in mind. In building cloud systems, it is
expected that the cloud system will be able to deal with high demands and unexpected events that affect the
reliability and performance of the system.
In this paper, chaos engineering is considered a heuristic method that can be used to build reliable cloud
systems. Chaos engineering is aimed at exposing weaknesses in systems that are in production. Chaos
engineering will help identify system weaknesses and strengths when a system is exposed to unexpected
knocks and shocks while it is in production.
Chaos engineering allows system developers and administrators to get insights into how the cloud system
will behave when it is exposed to unexpected occurrences.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
NETWORK MEDIA ATTENTION AND GREEN TECHNOLOGY INNOVATIONIJMIT JOURNAL
This paper will provide a novel empirical study for the relationship between network media attention and
green technology innovation and examine how network media attention can ease financing constraints. It
collected data from listed companies in China's heavy pollution industry and performed rigorous
regression analysis, in order to innovatively explore the environmental governance functions of the media.
It found that network media attention significantly promotes green technology innovation. By analyzing the
inner mechanism further, it found that network media attention can promote green innovation by easing
financing constraints. Besides, network media attention has a significant positive impact on green invention
patents while not affecting green utility model patents.
INCLUSIVE ENTREPRENEURSHIP IN HANDLING COMPETING INSTITUTIONAL LOGICS FOR DHI...IJMIT JOURNAL
Information System (IS) research advocates employing collaborative and loose coupling strategies to address contradictory issues to address diversified actors’ interests than the prescriptive and unilateral Information Technology (IT) governance mechanisms’, yet it is rarely depicting how managers employ these strategies in Health Information System (HIS) implementation, particularly in a resource-constrained setting where IS implementation activities have highly relied on multiple international organizations resources. This study explored how managers in resource-constrained settings employ collaborative IT governance mechanisms in the case of District Health Information System 2 (DHIS2) adoption with an interpretative case study approach and the institutional logic concept. The institutional logic concept was used to identify the major actors’ logics underpinning the DHIS2 adoption. The study depicted the importance of high-level officials' distance from the dominant systemic logic to consider new alternative, and to employ inclusive IT governance mechanisms which separated resource from the system that facilitated stakeholders’ collaboration in DHIS2 adoption based on their capacity and interest.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
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1. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
DOI : 10.5121/ijmit.2010.2401 1
COMMUNITY DETECTION IN THE COLLABORATIVE
WEB
Lylia Abrouk, David Gross-Amblard and Nadine Cullot
LE2I, UMR CNRS 5158
University of Burgundy, Dijon, France
lylia.abrouk@u-bourgogne.fr, david.gross-amblard@u-bourgogne.fr,
nadine.cullot@u-bourgogne.fr
ABSTRACT
Most of the existing social network systems require from their users an explicit statement of their
friendship relations. In this paper we focus on implicit Web communities and present an approach to
automatically detect them, based on user’s resource manipulations. This approach is dynamic as user
groups appear and evolve along with users interests over time. Moreover, new resources are dynamically
labelled according to who is manipulating them. Our proposal relies on the fuzzy K-means clustering
method and is assessed on large movie datasets.
KEYWORDS
Clustering, Data sharing, Information networks, user
distance, Web community.
1. INTRODUCTION
In the last decade, the basic Internet turns into a generic exchange platform, where any user
becomes a content provider by using spreading technologies like comments, blogs and wikis.
This new collaborative Web (called Web 2.0) hosts successful sites like Myspace, Facebook or
Flickr, that allow to build social networks based on professional relationship, interests, etc. This
so-called Social Web describes how people socialize or interact with each other. They suppose
from the user an explicit description of his/her social network.
However, a large amount of users communities also appear implicitly in various domains.
For example, any popular Web site about music will gather users with various musical tastes
and preferences: this also forms a huge community. But this coarse-grain community is in fact
composed of different pertinent and potentially disjoint sub-communities, all related to music
(for example the pop community, the punk community, and so on). Identifying precisely these
implicit communities would benefit to various actors, including Web site owners, on-line
advertisement agencies and above all, users of the system.
In this work, we propose an automatic community detection method that relies on the
resources manipulated by users. The method is generic as it depends only on a simple user-
defined tagging of resources. The method is also dynamic: communities evolve over time as
users change their resources annotations.
Finally, we also take into account the automatic tagging of a new resource, by analyzing how
it is used by communities. A building block of our method is the unsupervised classification
algorithm fuzzy-K-means [9].
2. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
2
The rest of the paper is organized as follows: Section 2 introduces our approach for automatic
community detection. This approach was implemented and tested on the movieLens data set, as
shown is Section 3. The related work is presented in Section 4. Finally, conclusion and
perspectives are presented in Section 5.
2. OUR APPROACH
Our method, in order to apply to a wide set of situations, in based on few hypothesis. We
consider a set P of users (persons) and a set R of resources (for example music files, videos,
news, etc.). First, we suppose that users express votes on some resources. This vote is not
necessarily explicit and can be obtained by monitoring user’s behavior (what item is listened, or
bought, or annotated, or recommended, etc.)
Votes are illustrated in a matrix MP: |P|x|R| defined as follows:
1 if pi likes rj:
MP(pi,rj)=
0 otherwise.
(1)
Where pi∈P and rj∈R. Second, we suppose to have a finite set T of tags (like pop, rock, punk,
etc.), and that each resource is annotated with a subset of these tags (potentially empty). We
define L(pi,tk) as a subset of R, where pi∈P and tk ∈T, the set of resources having tag tk liked by
user pi.
The main goals of this work are (1) to automatically detect communities and (2) to
automatically determine tags of new items. A community gathers persons having the same
interests, in the sense that they like resources that are tagged almost the same way. Our
approach deals with three key concepts that are presented below:
• Users distance: once a user has voted (implicitly) for resources, we define a user
distance that represents similarity of users interests.
• Community clustering: based on users’similarity, we construct users communities.
Each user belongs to one or several communities.
• Tags detection: each new resource is tagged automatically.
2.1. Users distance
Several works on collaborative recommendation systems and communities detection are
based on a similarity distance. Our distance measure is based on the number of tags users have
in common. Two users are considered closer if they appreciate the same resources, based on
their tags. The distance between two users pi and pj is defined by:
݀൫, ൯ ൌ 1 െ
1
|ܴ|
|ܮሺ, ݐሻ ת ܮ൫, ݐ൯|
|ܮሺ, ݐሻ ܮ൫, ݐ൯|
(2)
Distance closer to zero represents closer user friendship. Based on this measure, we can
construct users distance graph Gd:
Gd =<P, P X P X [0,1]> (3)
This graph is complete, undirected and each of its edges (pi, pj) is weighted by the
similarity distance between pi and pj.
3. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
3
2.2. Community clustering
Different classification techniques aiming at building users clusters can be envisioned. There are
two possible approaches for this classification: the supervised and the unsupervised one. The
first approach requires initially classified users to classify a new user. Among possible
algorithms used in this kind of approach, we can retain the k-nearest neighbors algorithm (k-
NN) [8] based on closest training examples. But this supervised approach appears a little
constraining because of the required manual construction of the social network. Indeed, it
imposes on users to create initially their profiles and to invite friends. The friends community
will then grow progressively.
Because of this strong constraint, we preferred the second type of classification - unsupervised
classification - that allows for automatic classification (that is, does not require training
examples).
Our goal is both to gather similar users (having the same interests) in the same community
(class) and to increase the distance between theses communities (classes). From time to time,
users votes can completely change, for example in musical items from Rock'n'roll to classical
music. Moreover, a person's interests may be composed of different tags. For this reason, we
chose an algorithm that allows a user to belong to several clusters simultaneously communities.
We chose a fuzzy extension of the K-means algorithm: fuzzy K-means [9], [10].
In this method, the number K of awaited classes has to be defined (we discuss the choice of K
later on). The method is based on the minimization of the following function (4), with K being
the number of clusters and N the number of persons, P={ pi | i ∈ [1..n]}, and m is a predefined
constant (generally m=2):
ܬ ൌ ݑ
ୀଵ
ே
ୀଵ
|ു െ ܿ |ു
(4)
with the constraint:
ݑ
ୀଵ
ൌ 1
(5)
Coefficient uij ∈ [0,1] is the membership degree of person pi in cluster j, and cj is the center
of cluster j.
The different steps of the fuzzy K-means algorithm are: (i) initialize matrix U=[uij], (ii) at
step k: compute centers Ck=[cj] (Equation 6), (iii) update the membership degree and (iv), if
||U(k+1)-Uk||<ε then stop else return to (ii).
ܿ ൌ
∑ ݑ
ே
ୀଵ
∑ ݑ
ே
ୀଵ
(6)
ݑ ൌ
1
∑ ൬
ു െ ܿ ു
ു െ ܿ ു
൰
ଶ
ିଵ
ୀଵ
(7)
The choice of the value of K can be done by the Web site owner, according to the desired
granularity. A natural option would be to choose K close to |T|, the size of the set of tags, if
these tags are supposed to provide a rich enough description vocabulary. If pairs of tags are
4. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
4
required for a convenient description, hence a value of K close to |T|² should be chosen, and so
on. For the sake of simplicity in the sequel we fixed K=|T|. In is noteworthy that semantically
related tags (synonyms) should be gathered in a precomputation phase. Otherwise, close users
could be shattered in different clusters. The algorithm result is a matrix MGp: |P|x|T| where
each element of MGp(i,j) is the degree of membership of pi in cluster j. Using this matrix, it is
now possible, for example, to invite a user to meet new friends in the same community, starting
with the closest user, according to the similarity distance. It is also possible to locate the closest
user to the center of the cluster: this user is representative of the whole community (a so-called
trendsetter). He/she can be the target of special attentions (access rights promotion on the Web
site forums, special offers, advertisements).
2.3. Tagging new resources
The previous clustering method can be invoked from time to time, and communities can be
updated according to the current user’s votes. This yields the dynamic flavor of the approach.
Another aspect of dynamicity is the problem of tagging new resources uploaded on the Web site
(by the Web site owner or by users). In this section we will tag a new resource according to the
users who like this resource. Thus, we also attach to users their representative tags. We start by
calculating user tags membership m(pi,tk), based on users votes:
݉ሺ, ݐሻ ൌ
|ܮሺ, ݐሻ|
|ܮሺሻ|
(8)
When a new resource rj appears, we update the users votes matrix MP(pi, rj) for each user pi.
Then, tags of the new resource are defined with regard to user’s votes. We calculate the tag
resource membership v(rj, tk) that represents the membership of the new resource rj ∈R to the tag
tk ∈ T (it may be seen as probability that tag tk represents resource rj). Hence, for each pi ∈ P
where rj ∈ L(pi), we define:
ݒ൫ݎ, ݐ൯ ൌ
1
|ܲ|
݉ሺ, ݐሻ,
,ೕאሺሻ
ݒ൫ݎ, ݐ൯
ൌ 1
(9)
Finally, among of all the potential tags for the resource, we select those tags that are
representative, using Receiver Operating Characteristics (ROC) curves. ROC curves are used to
evaluate classifiers: they provide information on the trade-off between the hit rate and the false
hit rate. In our context, it tests the system validity and finds pertinent threshold for each
potential tag.
Each item is represented by a vector, with items tags membership. ROC curve determines the
sensibility according to 1 - SP for different thresholds. Based on training test, we calculate
sensitivity (SE) and specificity (SP). Let:
• th be the threshold for tag ti,
• Rtp be the set of items having tags ti (annotated by expert) and having v(rj, tk) >= th,
(true positive),
Rfp be the set of items having tags ti (annotated by expert) and having v(rj, tk) <= th, (false
positive),
• Rtn be the set of items which don't have tags ti (annotated by expert) and have v(rj, tk)
<= th, (true negative),
5. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
5
• Rfn be the set of items which don't have tags ti (annotated by expert) such that v(rj, tk)
>= th, (false negative).
Then
ܵܧ ൌ
|ܴݐ|
|ܴݐ ܴ݂݊|
(10)
SP =
Rtn
Rtn + Rfp
(11)
The best threshold is point in the upper left corner (coordinate (1-SP=0, SE=1)) of the ROC
space, representing 100% sensitivity (no false negatives) and 100% specificity (no false
positives). This approach is illustrated on the next section.
3. EXEPERIMENTS
Due to lack of space, we focus in this section on the new item tags detection algorithm. We
tested our method on the MovieLens (ML)1 data set containing 100,000 ratings over 1,682
movies provided by 943 users. This data set contains movies rated with a numerical scale (1 to
5). Then, we transformed the value of this rating into a binary vote (where ratings greater than
two become "like" and otherwise "don't like"). Then (1) we computed users distance matrix to
construct users communities. In order to detect new item tags, (2) we compute the user tags
membership based on the 843 first items. (3) We tested our approach on the 100 last movies,
playing the role of new items. We present the results on six movie tags (1: comedy, 2: action, 3:
crime, 4:drama, 5:romance, 6: thriller).
3.1. Tags detection
For the 100 new resources, we calculate for each tag the value of v(rj, tk) which represents the
membership of the new resource rj to the genre tk .In order to detect tag, we calculate the
threshold th for each tag ti using ROC curves.
Table 1 represents Sensitivity (SE) and specificity (SP) for different threshold of "comedy" tag.
The optimal threshold is 0,14. It is the point in the upper left corner in the ROC curve.
Table 1. Comedy tag ROC table.
Threshold 1-Sp SE
0.08 0.957 1
0.1 0.903 0.968
0.12 0.772 0.937
0.14 0.337 0.656
0.16 0.196 0.343
0.18 0.087 0.06
0.2 0.044 0.06
0.22 0.033 0
0.24 0.011 0
1
http://www.grouplens.org/node/73
6. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
6
Figure 1 represents ROC curves for "comedy" and "action" tags.
Fig 1: tags roc curve.
We compute a ROC curve for each tag (Table 2). We can observe that the thresholds that
determine resource tags are generally between 0.1 and 0.2. The "Drama" tag is upper because
half of the resources have this specific tag.
Table 2: Threshold tags.
Tag Threshold
Comedy 0.14
Action 0.12
Crime 0.05
Drama 0.25
Romance 0.10
Thriller 0.10
3.2. Correlation between proposed and existing tags
Once our thresholds are calculated, we assess the correlation between our proposal and real
data set tags. We calculate the linear correlation coefficient r where xi represents the original
data set item tags membership (1 or 0) and yi represents our approach item tags membership.
ݎ ൌ
݊ ∑ ݔݕ െ ∑ ݔݕ
ඥ݊ ∑ ݔ
ଶ
െ ሺ∑ ݔሻଶ ඥ݊ ∑ ݕ
ଶ
െ ሺ∑ ݕሻଶ
(12)
Comedy ROC curve
-0,2
0
0,2
0,4
0,6
0,8
1
1,2
0 0,2 0,4 0,6 0,8 1 1,2
SE
1 - SP
th=0.14
Action ROC curve
0
0,2
0,4
0,6
0,8
1
1,2
-0,2 0 0,2 0,4 0,6 0,8 1 1,2
SE
1 - SP
7. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
7
Table 3: Correlation degrees.
Tag Correlation
Comedy 0.19
Action 0.34
Crime 0.25
Drama 0.25
Romance 0.17
Thriller 0.23
The correlation coefficient defines linear dependencies between x and y.
We use the Fisher test for significant of correlation (table 4). Correlation is significant for result
upper than 4. Significant result represents probability to have a false result less than 0.05.
Table 4: Fisher test result.
Tag Fisher Significant
Comedy 4.17 significant
Action 15.67 Very
significant
Crime 7.98 significant
Drama 7.95 significant
Romance 3.49 No significant
Thriller 6.87 significant
"Romance" tag is on the limit of the significativity. This is explain by the association with other
tags, it is appear with “comedy” or “drama” tags.
4. RELATED WORKS
Several works are devoted to community emergence. In this context, recommendation
systems like Amazon [1] handle communities implicitly, recommending items to users based on
the similarity between their interests.
Web sites generated by users are the cornerstone of Web 2.0 or collaborative Web: the goal
of this new Web is to transform users into contributors. Users not only add contents, but also
opinions and personal information. Another main aspect of this new Web is its social networks
(social relationships) which connect friends, even geographically distant. Social networks are
the grouping of individuals into specific communities. They make possible to look for comrades
or family members, but also to discover new friends, generally by affinities. We distinguish two
types of social networks: virtual network and social network online. The first one consists in the
discovery of new friends; the second one is a meeting place for existing friends. There is a large
number of social networking websites that focus on particular interests. For example,
SixDegrees.com2
was a social network which allowed users to expand their network based on
user’s profiles, and permitted to target a user community for specific services (music,
advertisement, etc.).
2
From 1997 to 2001.
8. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
8
The Myspace3
social network allows artists to upload their music and to create relations
between network members.
4.1. Recommendation system
Based on user’s behaviors, recommendation systems propose to the user a set of pertinent
playlist according to his profile. We can distinguish two methods: (i) collaborative methods
creating community of users with similar interests and recommend music listened by the same
community. (ii) Content based methods.
Liu and al. [2] take into account the changes of user’s interest in time by adding time
parameter in order to improve the recommendation. The algorithm generates a decision tree to
represents user's votes. The method is divided into three main steps: (1) users give to the system
personal information, (2) the system constructs users communities and initial music lists, (3)
formalized recommendations are generated by the system, using decision tree classification, in
order to recommend music to the user at the current time.
This method solves the cold-start problem for new users but, in practice, this method is not
completely satisfying because few users give all personal information like music genre.
Celma and al. [3] use the FOAF standard description and content based description to
recommend music resources. The Friend of a Friend (FOAF) project4
consists in creating a Web
of machine-readable pages describing people in order to connect social Web sites.
The music recommendation system extracts users' interests from a FOAF profile, detects
artists by relationships and finally selects similar artists by relevance.
Firan and al. [4] propose a recommendation algorithm based on user profiles (tags). The tag
usage is analyzed on last.fm5
music site. Authors define three types of algorithms: (i)
collaborative filtering based on tracks where users rank tracks; the cold start problem appears in
this algorithm type, (ii) collaborative filtering based on tags and search based on tags.
An hybrid method (collaborative aspect and content) proposed by Yoshi and al. [5] uses a
probabilistic model to integrate rating and content data using a Bayesian network to perform
classical methods.
4.2. Emergent community
Cattuto and al [6] present an approach experimented on del.icio.us6
web site data where
community structure exists in tagging data collection to construct weighted networks of
resources. In this context the resources similarity is represented by the overlap of tag sets. To
take into account tag frequency, the TF-IDF weight is used. In [6], authors propose to detect
virtual communities of users with similar music interests in order to create a music channel for
the community. They use Pearson correlation coefficient to define the similarity measure.
Clustering methods are used and estimated.
Several techniques are applied in the collaborative Web to create users communities and
recommendations systems. Measures are generally based on user profiles. Current methods take
into account user needs and consider that the new resources contain metadata (tags, type, …).
3
http://www.myspace.com/
4
http://www.foaf-project.org/
5
www.last.fm
6
http://delicious.com/
9. International Journal of Managing Information Technology (IJMIT) Vol.2, No.4, November 2010
9
5. CONCLUSION
In this work we proposed a dynamic method for automatic community detection and automatic
tagging of new resources. This work is currently under integration into the NEUMA platform7
,
an open system for communities manipulating music in the symbolic format (like MusicXML).
Our future work will extend the notion of simple tags with more sophisticated ontology on the
one side [11], and will take into account the dynamic of communities over time on the other
side.
ACKNOWLEDGEMENTS
This work is partially supported by the French ANR fund Content & Interactions -- NEUMA
(2009-2011).
REFERENCES
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filtering”, Internet Computing, IEEE, Vol. 7, No. 1. pp. 76-80., 2003.
[2] Ning-Han Liu, Szu-Wei Lai, Chien-Yi Chen, Shu-Ju Hsieh,Adaptive “Music Recommendation
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[3] Celma, O. and Ramairez, M. and Herrera, P.
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Scripting for the Semantic Web co-located with the 2nd European Semantic Web Conference, 2005.
[4] Firan, C S and Nejdl, W and Paiu, R. “The Benefit of Using Tag-Based Profile”, Proceedings
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[5] Yoshii, K and Goto, M and Komatani, K and Ogata, T and Okuno, H, G. “Hybrid collaborative
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Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR), 2006.
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Structure In Social Tagging Systems”. Advances in Complex Systems (ACS), pp 597-608, 2008.
[7] Anglade, A and Tiemann, M and Vignoli, F. “Virtual communities for creating shared music
channels”', Proceedings of ISMIR, pages 95-100, 2007.
[8] Dasarathy, B. V. “Nearest Neighbor (NN) Norms--NN Pattern Classification Techniques”, Los
Alamitos, CA: IEEE Computer Society Press. 1991.
[9] Dun, J. C. “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-
Separated Clusters”, Journal of Cybernetics 3: 32-57.
[10] Bezdek, J. C. “Pattern Recognition with Fuzzy Objective Function Algoritms”, Plenum Press,
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[11] Abrouk, L and Gouaich, A “Automatic Annotation Using Citation Links and Co-citation
Measure: Application to the Water Information System”, Proceedings of ASWC06, 2006.
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http://neuma.irpmf-cnrs.fr