Julia Kiseleva's slides for PhD defense on June 13 2016.
The thesis is available by the following link -- https://www.researchgate.net/publication/303285745_Using_Contextual_Information_to_Understand_Searching_and_Browsing_Behavior
Predicting User Satisfaction with Intelligent AssistantsJulia Kiseleva
There is a rapid growth in the use of voice-controlled intelligent
personal assistants on mobile devices, such as Microsoft’s Cortana,
Google Now, and Apple’s Siri.
They significantly change the way users interact with search systems,
not only because of the voice control use and touch gestures,
but also due to the dialogue-style nature of the interactions and their
ability to preserve context across different queries. Predicting success
and failure of such search dialogues is a new problem, and
an important one for evaluating and further improving intelligent
assistants. While clicks in web search have been extensively used
to infer user satisfaction, their significance in search dialogues is
lower due to the partial replacement of clicks with voice control,
direct and voice answers, and touch gestures.
In this paper, we propose an automatic method to predict user
satisfaction with intelligent assistants that exploits all the interaction
signals, including voice commands and physical touch gestures
on the device.
First, we conduct an extensive user study to measure user satisfaction
with intelligent assistants, and simultaneously record all
user interactions. Second, we show that the dialogue style of interaction
makes it necessary to evaluate the user experience at the
overall task level as opposed to the query level. Third, we train a
model to predict user satisfaction, and find that interaction signals
that capture the user reading patterns have a high impact: when including
all available interaction signals, we are able to improve the
prediction accuracy of user satisfaction from 71% to 81% over a
baseline that utilizes only click and query features.
Detecting Good Abandonment in Mobile SearchJulia Kiseleva
Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered
as leading to user dissatisfaction. However, there are many
cases where a user may not click on any search result page
(SERP) but still be satised. This scenario is referred to
as good abandonment and presents a challenge for most ap-
proaches measuring search satisfaction, which are usually
based on clicks and dwell time. The problem is exacerbated
further on mobile devices where search providers try to in-
crease the likelihood of users being satised directly by the
SERP. This paper proposes a solution to this problem us-
ing gesture interactions, such as reading times and touch
actions, as signals for dierentiating between good and bad
abandonment. These signals go beyond clicks and charac-
terize user behavior in cases where clicks are not needed to
achieve satisfaction. We study different good abandonment
scenarios and investigate the dierent elements on a SERP
that may lead to good abandonment. We also present an
analysis of the correlation between user gesture features and
satisfaction. Finally, we use this analysis to build models to
automatically identify good abandonment in mobile search
achieving an accuracy of 75%, which is significantly better
than considering query and session signals alone. Our fundings have implications for the study and application of user
satisfaction in search systems.
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Na...eMadrid network
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Nacional de Educación a Distancia: Mecanismos de reputación en MOOCs. 2015-06-30
Predicting User Satisfaction with Intelligent AssistantsJulia Kiseleva
There is a rapid growth in the use of voice-controlled intelligent
personal assistants on mobile devices, such as Microsoft’s Cortana,
Google Now, and Apple’s Siri.
They significantly change the way users interact with search systems,
not only because of the voice control use and touch gestures,
but also due to the dialogue-style nature of the interactions and their
ability to preserve context across different queries. Predicting success
and failure of such search dialogues is a new problem, and
an important one for evaluating and further improving intelligent
assistants. While clicks in web search have been extensively used
to infer user satisfaction, their significance in search dialogues is
lower due to the partial replacement of clicks with voice control,
direct and voice answers, and touch gestures.
In this paper, we propose an automatic method to predict user
satisfaction with intelligent assistants that exploits all the interaction
signals, including voice commands and physical touch gestures
on the device.
First, we conduct an extensive user study to measure user satisfaction
with intelligent assistants, and simultaneously record all
user interactions. Second, we show that the dialogue style of interaction
makes it necessary to evaluate the user experience at the
overall task level as opposed to the query level. Third, we train a
model to predict user satisfaction, and find that interaction signals
that capture the user reading patterns have a high impact: when including
all available interaction signals, we are able to improve the
prediction accuracy of user satisfaction from 71% to 81% over a
baseline that utilizes only click and query features.
Detecting Good Abandonment in Mobile SearchJulia Kiseleva
Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered
as leading to user dissatisfaction. However, there are many
cases where a user may not click on any search result page
(SERP) but still be satised. This scenario is referred to
as good abandonment and presents a challenge for most ap-
proaches measuring search satisfaction, which are usually
based on clicks and dwell time. The problem is exacerbated
further on mobile devices where search providers try to in-
crease the likelihood of users being satised directly by the
SERP. This paper proposes a solution to this problem us-
ing gesture interactions, such as reading times and touch
actions, as signals for dierentiating between good and bad
abandonment. These signals go beyond clicks and charac-
terize user behavior in cases where clicks are not needed to
achieve satisfaction. We study different good abandonment
scenarios and investigate the dierent elements on a SERP
that may lead to good abandonment. We also present an
analysis of the correlation between user gesture features and
satisfaction. Finally, we use this analysis to build models to
automatically identify good abandonment in mobile search
achieving an accuracy of 75%, which is significantly better
than considering query and session signals alone. Our fundings have implications for the study and application of user
satisfaction in search systems.
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Na...eMadrid network
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Nacional de Educación a Distancia: Mecanismos de reputación en MOOCs. 2015-06-30
We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
An evaluation of SimRank and Personalized PageRank to build a recommender sys...Paolo Tomeo
The Web of Data is the natural evolution of the World Wide Web from a set of interlinked documents to a set of interlinked entities. It is a graph of information resources interconnected by semantic relations, thereby yielding the name Linked Data. The proliferation of Linked Data is for sure an opportunity to create a new family of data-intensive applications such as recommender systems. In particular, since content-based recommender systems base on the notion of similarity between items, the selection of the right graph-based similarity metric is of paramount importance to build an effective recommendation engine. In this paper, we review two existing metrics, SimRank and PageRank, and investigate their suitability and performance for computing similarity between resources in RDF graphs and investigate their usage to feed a content-based recommender system. Finally, we conduct experimental evaluations on a dataset for musical artists and bands recommendations thus comparing our results with two other content-based baselines
measuring their performance with precision and recall, catalog coverage, items distribution and novelty metrics.
Catalog University Pub talk: Leveraging browsing behavior to improve catalog ...Sarah Fletcher
Working with Digital Browsing Behavior to Improve Customer Response
Incorporating digital predictors into a mailing strategy can help you make better print mailing decisions, and can increase revenue by two to three times. CohereOne is pioneering overlaying online browsing behavior on traditional circulation planning to target customers who would traditionally be considered unmailable in catalog circulation planning yet may actually be responding well within the response range needed to be profitable.
Smart multichannel merchants are leveraging their customer’s online behavior to see if they are still actively engaged with the brand even though they may not have made a recent catalog purchase. the information gleaned can also identify segments that will not respond and should be suppressed. Increasing catalog circulation to customers who are likely to make a catalog purchase and suppressing customers who are no longer engaged is the one-two punch that can really improve your bottom line.
Two Case Studies
CohereOne shares two case studies showing how they found opportunities for both increasing reactivation circulation and suppressing unproductive names. One retailer incorporated digital predictors and reactivated their older buyers by 83%. That’s pretty significant when you consider the cost of acquiring new buyers.
Join Travis Seaton, Vice President of Client Services, and Jude Hoffner, Vice President of Digital Product Management at CohereOne, as they explore how traditional selection criteria in circulation management (recency, frequency, monetary) is making room for a more targeted and ecommerce-centric approach.
A beginner's guide to the world of online advertising. Find out what happens under the hood; find out how online advertising generates revenue and for whom; find out who the players are and their roles in the world of online advertisting.
We Are Social's Guide to Social, Digital and Mobile Around the World (Feb 2013)We Are Social Singapore
This is the February 2013 edition of We Are Social Singapore’s guide to Social, Digital and Mobile around the world. You'll find more in this series of reports at http://wearesocial.sg/tag/sdmw
This report presents all the key statistics, data and behavioural indicators for social, digital and mobile channels around the world. Alongside regional pictures that capture the stats for every nation on Earth, we also present in-depth analyses for 24 of the world's largest economies: Argentina, Australia, Brazile, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Nigeria, Poland, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Turkey, Thailand, the UAE, the UK, and the USA. For other reports in this series, please visit http://wearesocial.sg/tag/sdmw
We Are Social's comprehensive new report covers internet, social media and mobile usage statistics from all over the world. It contains more than 350 infographics, including global snapshots, regional overviews, and in-depth profiles of 30 of the world's largest economies. For a more insightful analysis of these numbers, please visit http://bit.ly/SDMW2015
We Are Social's comprehensive new Digital in 2016 report presents internet, social media, and mobile usage statistics and trends from all over the world. It contains more than 500 infographics, including global data snapshots, regional overviews, and in-depth profiles of the digital landscapes in 30 of the world's key economies. For a more insightful analysis of the numbers contained in this report, please visit http://bit.ly/DSM2016ES.
UX playbook: Real world user exercisesInVision App
Users are full of surprises. And they have a way of finding confusing spots in a product even if your team meticulously planned and designed it. In this session, our very own Clark Wimberly walked us through a number of fun and challenging exercises aimed at keeping users happy.
PRPL Information Architect Tricia D'Antin explains the thought process behind merging business goals with user goals through user experience (UX) design.
Store Front Optimization | David Henry, Monster.com | iStrategy, LondoniStrategy
How to optimize your ecommerce store front.
Presented by David Henry, VP Digital Media and Marketing Europe of Monster.com during iStrategy London 2010.
We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
An evaluation of SimRank and Personalized PageRank to build a recommender sys...Paolo Tomeo
The Web of Data is the natural evolution of the World Wide Web from a set of interlinked documents to a set of interlinked entities. It is a graph of information resources interconnected by semantic relations, thereby yielding the name Linked Data. The proliferation of Linked Data is for sure an opportunity to create a new family of data-intensive applications such as recommender systems. In particular, since content-based recommender systems base on the notion of similarity between items, the selection of the right graph-based similarity metric is of paramount importance to build an effective recommendation engine. In this paper, we review two existing metrics, SimRank and PageRank, and investigate their suitability and performance for computing similarity between resources in RDF graphs and investigate their usage to feed a content-based recommender system. Finally, we conduct experimental evaluations on a dataset for musical artists and bands recommendations thus comparing our results with two other content-based baselines
measuring their performance with precision and recall, catalog coverage, items distribution and novelty metrics.
Catalog University Pub talk: Leveraging browsing behavior to improve catalog ...Sarah Fletcher
Working with Digital Browsing Behavior to Improve Customer Response
Incorporating digital predictors into a mailing strategy can help you make better print mailing decisions, and can increase revenue by two to three times. CohereOne is pioneering overlaying online browsing behavior on traditional circulation planning to target customers who would traditionally be considered unmailable in catalog circulation planning yet may actually be responding well within the response range needed to be profitable.
Smart multichannel merchants are leveraging their customer’s online behavior to see if they are still actively engaged with the brand even though they may not have made a recent catalog purchase. the information gleaned can also identify segments that will not respond and should be suppressed. Increasing catalog circulation to customers who are likely to make a catalog purchase and suppressing customers who are no longer engaged is the one-two punch that can really improve your bottom line.
Two Case Studies
CohereOne shares two case studies showing how they found opportunities for both increasing reactivation circulation and suppressing unproductive names. One retailer incorporated digital predictors and reactivated their older buyers by 83%. That’s pretty significant when you consider the cost of acquiring new buyers.
Join Travis Seaton, Vice President of Client Services, and Jude Hoffner, Vice President of Digital Product Management at CohereOne, as they explore how traditional selection criteria in circulation management (recency, frequency, monetary) is making room for a more targeted and ecommerce-centric approach.
A beginner's guide to the world of online advertising. Find out what happens under the hood; find out how online advertising generates revenue and for whom; find out who the players are and their roles in the world of online advertisting.
We Are Social's Guide to Social, Digital and Mobile Around the World (Feb 2013)We Are Social Singapore
This is the February 2013 edition of We Are Social Singapore’s guide to Social, Digital and Mobile around the world. You'll find more in this series of reports at http://wearesocial.sg/tag/sdmw
This report presents all the key statistics, data and behavioural indicators for social, digital and mobile channels around the world. Alongside regional pictures that capture the stats for every nation on Earth, we also present in-depth analyses for 24 of the world's largest economies: Argentina, Australia, Brazile, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Nigeria, Poland, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Turkey, Thailand, the UAE, the UK, and the USA. For other reports in this series, please visit http://wearesocial.sg/tag/sdmw
We Are Social's comprehensive new report covers internet, social media and mobile usage statistics from all over the world. It contains more than 350 infographics, including global snapshots, regional overviews, and in-depth profiles of 30 of the world's largest economies. For a more insightful analysis of these numbers, please visit http://bit.ly/SDMW2015
We Are Social's comprehensive new Digital in 2016 report presents internet, social media, and mobile usage statistics and trends from all over the world. It contains more than 500 infographics, including global data snapshots, regional overviews, and in-depth profiles of the digital landscapes in 30 of the world's key economies. For a more insightful analysis of the numbers contained in this report, please visit http://bit.ly/DSM2016ES.
UX playbook: Real world user exercisesInVision App
Users are full of surprises. And they have a way of finding confusing spots in a product even if your team meticulously planned and designed it. In this session, our very own Clark Wimberly walked us through a number of fun and challenging exercises aimed at keeping users happy.
PRPL Information Architect Tricia D'Antin explains the thought process behind merging business goals with user goals through user experience (UX) design.
Store Front Optimization | David Henry, Monster.com | iStrategy, LondoniStrategy
How to optimize your ecommerce store front.
Presented by David Henry, VP Digital Media and Marketing Europe of Monster.com during iStrategy London 2010.
This is the presentation that was delivered at BCS-IRSG ECIR 2018. This work proposes an extension to the Complex Searcher Model (CSM), enabling us to model user interactions on a Search Engine Results Page (SERP). Grounded by Information Foraging Theory (IFT), we propose a new stopping decision point within the CSM. Results of simulations show that this new stopping decision point improves the realism of searcher models, and suggests that models and measures used in Information Retrieval research need to be u
* Differences between Websites and Web Applications
* Research Techniques for Knowing Your Users
* Task Analysis
* UI/UX Design Principles for Web Applications
Key Lime Interactive's Principal Researcher/Director, Andrew Schall, and Facebook User Researcher, Jennifer Romano Bergstrom, take a deep dive into eye tracking the mobile user experience. View the slides from the webinar.
Today, I had the big honor to give the opening keynote at the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020), being held virtually. HCOMP is the home of the human computation and crowdsourcing community working on frameworks, methods and systems that bring together people and machine intelligence to achieve better results. I decided to totally revamp a previous talk to focus on so-called "human in the loop" and showed how we incorporate human in the loop to personalise at scale, with some of the research at Spotify. Sharing the slides for general interests.
Newbie in E-commerce Indonesia? Confuse to understand their habbit? Here i present to you, a quick guide to understand their behavior. This is a compilation of market research and study of consumer e-commerce in Indonesia. E-mail me at andersonpetra91@gmail.com if you have further questions. Thank you! :)
Introduction to Microsoft Search #SRC101 #365EduCon 20211214Kanwal Khipple
Microsoft Search is looking to bring the search experiences across all Microsoft 365 services together into a single unified experience.
Attend this session to learn how the experience impacts your users, how you can configure it as well as scenarios where you should customize it.
The information available on internet is in unsystematic manner. With the help of available browsers, user can get their data but that too are not relevant. To get relevant results, users’ interest should be considered. But no available browser is considering users’ interest in browsing the internet. There are some indicators that are used to indicate the users’ browsing behaviour over the internet. These indicators are called as
implicit indicators and explicit indicators. In this paper, a tool is proposed that will store the users’ browsing behaviour indicators. These indicators may be used in future to analyze the web page visited by user.
Optimizing Mobile UX Design Webinar Presentation SlidesUserZoom
Optimizing Mobile UX Design: Webinar on Mobile User Experience Research Methods & Tools
Most businesses are investing in mobile apps and mobile commerce. Recently, more emphasis has been placed on the interactive experiences users have on mobile devices
To explain how to optimize the user experience on mobile interfaces, UserZoom will be joined by special guest User Centric in a complimentary webinar. The webinar will focus on how user experience research methods and tools can add extremely valuable insights into the design process and help brands optimize their mobile site or application’s performance. Attendees will hear presentations from the following experts:
Gavin Lew, Managing Director, User Centric
Gavin’s 20 years of experience in corporate and academic environments have given him a strong foundation in user-centered design and evaluation. In addition to managing User Centric, he holds particular expertise in mobile technology, among other interests. He is a frequent presenter at national conferences, adjunct faculty member at DePaul University and Northwestern University’s Feinberg School of Medicine, and the inventor of several patents.
Kim Oslob, UserZoom Director of Client Services
Kim has extensive experience with both qualitative and quantitative UX Research through her work at Claris (now Filemaker), Macromedia (now Adobe) and VistoCorp (now Good). She has managed projects with companies in the mobile space such as Vodafone, Nokia, Sprint, and Roger’s Wireless to improve the user experience of over 10 different mobile operating systems.
Microsoft Search is looking to bring the search experiences across all Microsoft 365 services together into a single unified experience. Attend this session to learn how the experience impacts your users, how you can configure it as well as scenarios where you should customize it.
Similar to Using Contextual Information to Understand Searching and Browsing Behavior (20)
Understanding User Satisfaction with Intelligent AssistantsJulia Kiseleva
Voice-controlled intelligent personal assistants, such as Cortana,
Google Now, Siri and Alexa, are increasingly becoming a part of
users’ daily lives, especially on mobile devices. They introduce
a significant change in information access, not only by introducing
voice control and touch gestures but also by enabling dialogues
where the context is preserved. This raises the need for evaluation
of their effectiveness in assisting users with their tasks. However,
in order to understand which type of user interactions reflect different
degrees of user satisfaction we need explicit judgements. In this
paper, we describe a user study that was designed to measure user
satisfaction over a range of typical scenarios of use: controlling a
device, web search, and structured search dialogue. Using this data,
we study how user satisfaction varied with different usage scenarios
and what signals can be used for modeling satisfaction in the
different scenarios. We find that the notion of satisfaction varies
across different scenarios, and show that, in some scenarios (e.g.
making a phone call), task completion is very important while for
others (e.g. planning a night out), the amount of effort spent is key.
We also study how the nature and complexity of the task at hand
affects user satisfaction, and find that preserving the conversation
context is essential and that overall task-level satisfaction cannot
be reduced to query-level satisfaction alone. Finally, we shed light
on the relative effectiveness and usefulness of voice-controlled intelligent
agents, explaining their increasing popularity and uptake
relative to the traditional query-response interaction.
Behavioral Dynamics from the SERP’s Perspective: What are Failed SERPs and Ho...Julia Kiseleva
Web search is always in a state of flux: queries, their intent, and the
most relevant content are changing over time, in predictable and unpredictable
ways. Modern search technology has made great strides
in keeping up to pace with these changes, but there remain cases of
failure where the organic search results on the search engine result
page (SERP) are outdated, and no relevant result is displayed.
Failing SERPs due to temporal drift are one of the greatest frustrations
of web searchers, leading to search abandonment or even
search engine switch. Detecting failed SERPs timely and providing
access to the desired out-of-SERP results has huge potential to improve
user satisfaction. Our main findings are threefold: First, we
refine the conceptual model of behavioral dynamics on the web by
including the SERP and defining (un)successful SERPs in terms of
observable behavior. Second, we analyse typical patterns of temporal
change and propose models to predict query drift beyond the
current SERP, and ways to adapt the SERP to include the desired
results. Third, we conduct extensive experiments on real world
search engine traffic demonstrating the viability of our approach.
Our analysis of behavioral dynamics at the SERP level gives new
insight in one of the primary causes of search failure due to temporal
query intent drifts. Our overall conclusion is that the most
detrimental cases in terms of (lack of) user satisfaction lead to the
largest changes in information seeking behavior, and hence to observable
changes in behavior we can exploit to detect failure, and
moreover not only detect them but also resolve them.
Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User P...Julia Kiseleva
Recommendation based on user preferences is a common
task for e-commerce websites. New recommendation algorithms
are often evaluated by offline comparison to baseline
algorithms such as recommending random or the most
popular items. Here, we investigate how these algorithms
themselves perform and compare to the operational production
system in large scale online experiments in a real-world
application. Specifically, we focus on recommending travel
destinations at Booking.com, a major online travel site, to
users searching for their preferred vacation activities. To
build ranking models we use multi-criteria rating data provided
by previous users after their stay at a destination. We
implement three methods and compare them to the current
baseline in Booking.com: random, most popular, and Naive
Bayes. Our general conclusion is that, in an online A/B test
with live users, our Naive-Bayes based ranker increased user
engagement significantly over the current online system
Predicting Current User Intent with Contextual Markov ModelsJulia Kiseleva
Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context
discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us
to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of
user intentions with contextual Markov models.
The talk at Twente University on 28 July 2014 Julia Kiseleva
Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Using Contextual Information to Understand Searching and Browsing Behavior
1. Using Contextual Information
to Understand
Searching and Browsing Behavior
Julia Kiseleva
Eindhoven University of Technology
Eindhoven, The Netherlands, June 2016
8. Contextual Information
Explicit Context Implicit Context
Contextual Situations
(Android Tablet, Weekend)
Photo credit: Delwin Steven Campbell
via Visualhunt.com / CC BY
10. Our Main Research Goal
How to
use
contextual information
in order to
understand
users’ searching and
browsing
behavior on the web?
Improve Online
User Experience
12. Destination Finder
Chapter 3 ‘Contextual Profiles’.
L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015
J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
13. Destination Finder
Chapter 3 ‘Contextual Profiles’.
L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015
J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
14. Destination Finder
Chapter 3 ‘Contextual Profiles’.
L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015
J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
15. Destination Finder
Optimized Ranking of Destinations
Using Contextual Situations
Increased User Engagement
(Click Trough Rate +3.7%)
Chapter 3 ‘Contextual Profiles’.
L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015
J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
18. Changes in User Satisfaction
Want to go to
CIKM conference
QUERY SERP
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
19. Changes in User Satisfaction
QUERY SERP
,
Dynamic over Time
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
20. Changes in User Satisfaction
Time
Satisfaction
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY
, SERP
21. Changes in User Satisfaction
Time
#Reformulations
~
Satisfaction
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
2013
Oct NovSepAugJul
QUERY
, SERP
22. Changes in User Satisfaction
Before November 2013 After November 2013
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY= ‘flawless’
23. Changes in User Satisfaction
Before November 2013 After November 2013
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’
J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014
J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY= ‘flawless’
25. Q1: how is the weather in Chicago
Q2: how is it this weekend
Q3: find me hotels
Q4: which one of these is the cheapest
Q5: which one of these has at least 4 stars
Q6: find me directions from the Chicago airport to
number one
User’s dialogue
with Cortana:
Task is “Finding
a hotel in
Chicago”
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
26. Q1: find me a pharmacy nearby
Q2: which of these is highly rated
Q3: show more information about number 2
Q4: how long will it take me to get there
Q5: Thanks
User’s dialogue
with Cortana:
Task is “Finding
a pharmacy”
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
27. Cortana:
“Here are ten
restaurants
near you”
Cortana:
“Here are ten
restaurants near
you that have
good reviews”
Cortana:
“Getting you
direction to the
Mayuri Indian
Cuisine”
User:
“show
restauran
ts near
me”
User:
“show the
best ones”
User:
“show
directions
to the
second
one”
28. Cortana:
“Here are ten
restaurants
near you”
Cortana:
“Here are ten
restaurants near
you that have
good reviews”
Cortana:
“Getting you
direction to the
Mayuri Indian
Cuisine”
User:
“show
restauran
ts near
me”
User:
“show the
best ones”
User:
“show
directions
to the
second
one”
No Clicks
???
29. Cortana:
“Here are ten
restaurants
near you”
Cortana:
“Here are ten
restaurants near
you that have
good reviews”
Cortana:
“Getting you
direction to the
Mayuri Indian
Cuisine”
User:
“show
restauran
ts near
me”
User:
“show the
best ones”
User:
“show
directions
to the
second
one”
SAT? SAT?
SAT
?
Overall
SAT?
? SAT? SAT?
SAT
?
30. Acoustic Similarity
Phonetic Similarity
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
31. Tracking User Interaction
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
32. 3 seconds 6 seconds
33% of
ViewPort
66% of
ViewPort
ViewPortHeight
2 seconds
20% of
ViewPort
1s 4s 0.4s 5.4s+ + =
Tracking User Interaction
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
33. Quality of Interaction Model
Method Accuracy (%) Average F1 (%)
Baseline 70.62 61.38
Interaction Model 80.81*
(14.43)
79.08*
(28.83)
* Statistically significant improvement (p < 0,05 )
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’
J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016
J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
34. • Contextual information should be taken into account
to understand web and mobile users’ behavior
• Analyzing behavioral signals over time is needed to
detect changes in user satisfaction with web search
• Touch signals are crucial for inferring user
satisfaction with intelligent assistants on mobile
devices
Conclusion
Editor's Notes
Examples of contexts
Examples of understanding
What is searching and Browsing behavior
Search
Satisfaction vs dsat
Remove the text
Browsing
Examples of contexts
Examples of understanding
What is searching and Browsing behavior
Color
Remove the affliations
Emph. Implicit and explicit
Try to discover
Examples of contexts
Examples of understanding
What is searching and Browsing behavior
Replace to use
Think how to make it pict.
To improve user expertise