[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researchers have explored how emotions take effect in recommendations -- especially the usage of the emotional variables other than the effectiveness alone. In this paper, we explore the role of emotions in context-aware recommendation algorithms. More specifically, we evaluate two types of popular context-aware recommendation algorithms -- context-aware splitting approaches and differential context modeling. We examine predictive performance, and also explore the usage of emotions to discover how emotional features interact with those context-aware recommendation algorithms in the recommendation process.
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...YONG ZHENG
Yong Zheng. "Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms". ACM RecSys Doctoral Symposium, Proceedings of the 8th ACM Conference on Recommender Systems (ACM RecSys 2014), pp. 437-440, Silicon Valley, CA, USA, Oct 2014 [Doctoral Symposium, Acceptance rate: 47%]
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.
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researchers have explored how emotions take effect in recommendations -- especially the usage of the emotional variables other than the effectiveness alone. In this paper, we explore the role of emotions in context-aware recommendation algorithms. More specifically, we evaluate two types of popular context-aware recommendation algorithms -- context-aware splitting approaches and differential context modeling. We examine predictive performance, and also explore the usage of emotions to discover how emotional features interact with those context-aware recommendation algorithms in the recommendation process.
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...YONG ZHENG
Yong Zheng. "Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms". ACM RecSys Doctoral Symposium, Proceedings of the 8th ACM Conference on Recommender Systems (ACM RecSys 2014), pp. 437-440, Silicon Valley, CA, USA, Oct 2014 [Doctoral Symposium, Acceptance rate: 47%]
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.
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...YONG ZHENG
Yong Zheng, Mayur Agnani, Mili Singh. “Identifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filtering”. Proceedings of The 6th ACM Conference on Research in Information Technology (RIIT), Rochester, NY, USA, October, 2017
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
Machine Learning based Hybrid Recommendation System
• Developed a Hybrid Movie Recommendation System using both Collaborative and Content-based methods
• Used linear regression framework for determining optimal feature weights from collaborative data
• Recommends movie with maximum similarity score of content-based data
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
PROJECT REPORT
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...YONG ZHENG
Yong Zheng, Mayur Agnani, Mili Singh. “Identifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filtering”. Proceedings of The 6th ACM Conference on Research in Information Technology (RIIT), Rochester, NY, USA, October, 2017
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
Machine Learning based Hybrid Recommendation System
• Developed a Hybrid Movie Recommendation System using both Collaborative and Content-based methods
• Used linear regression framework for determining optimal feature weights from collaborative data
• Recommends movie with maximum similarity score of content-based data
Summary of a Recommender Systems Survey paperChangsung Moon
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, “Recommender Systems Survey,” Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
PROJECT REPORT
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
A General Architecture for an Emotion-aware Content-based Recommender SystemLucio Narducci
A General Architecture for an Emotion-aware Content-based Recommender System
Fedelucio Narducci, Marco De Gemmis, Pasquale Lops
3rd Empire Workshop
RecSys 2015, Vienna, Austria, 16-20 September 2015
Yusuke Goto (iwate Pref. Univ.) and Shingo Takahashi (Waseda Univ.)
How Scenario Analysis Can Contribute to ABMS Validation
The 7th International Workshop on Agent-based Approaches in Economic and Social Complex Systems
January 17, 2012 (Osaka, Japan)
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
Audio Music Similarity is a task within Music Information Retrieval that deals with systems that retrieve songs musically similar to a query song according to their audio content. Evaluation experiments are the main scientific tool in Information Retrieval to determine what systems work better and advance the state of the art accordingly. It is therefore essential that the conclusions drawn from these experiments are both valid and reliable, and that we can reach them at a low cost. This dissertation studies these three aspects of evaluation experiments for the particular case of Audio Music Similarity, with the general goal of improving how these systems are evaluated. The traditional paradigm for Information Retrieval evaluation based on test collections is approached as an statistical estimator of certain probability distributions that characterize how users employ systems. In terms of validity, we study how well the measured system distributions correspond to the target user distributions, and how this correspondence affects the conclusions we draw from an experiment. In terms of reliability, we study the optimal characteristics of test collections and statistical procedures, and in terms of efficiency we study models and methods to greatly reduce the cost of running an evaluation experiment.
Data Analyst, Data Scientist, and Data Engineer are three distinct roles within the field of data and analytics, each with its own set of responsibilities and skill requirements. Here's a brief overview of each role:
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The Systemic Design Toolkit represents a formalized set of methods and research tools designed by Namahn and developed with collaboration by me (SDA) and Alex Ryan of MaRS. The Toolkit can be discovered at https://www.systemicdesigntoolkit.org/
Similar to [EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization (20)
[WI 2014]Context Recommendation Using Multi-label ClassificationYONG ZHENG
Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected. We believe that context recommenders can be used as another set of tools to assist users' decision making. We formulate the context recommendation problem and discuss the motivation behind and possible applications of the concept. We identify two general classes of algorithms to solve this problem: direct context prediction and indirect context recommendation. Furthermore, we present and evaluate several direct context prediction algorithms based on multi-label classification (MLC). Our experiments demonstrate that the proposed approaches outperform the baseline methods, and also that personalization is required to enhance the effectiveness of context recommenders.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
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[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...YONG ZHENG
Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context – identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...YONG ZHENG
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[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization
1. Adapt to Emotional Reactions In Context-
aware Personalization
Yong Zheng
Illinois Institute of Technology
Chicago, IL, USA
The 4th Workshop on Emotions and Personality in
Personalized Systems (EMPIRE), September 16, 2016
4. Summary
4
We use emotions as context in recommender systems
• Recommender Systems
• Context-aware Recommender Systems
• What is Context?
Adapt to emotional reactions
• Emotional reactions
• The LDOS-CoMoDa Data
• Predictive Models Utilizing Emotional Reactions
• Findings and Results
5. Summary
5
We use emotions as context in recommender systems
• Recommender Systems
• Context-aware Recommender Systems
• What is Context?
Adapt to emotional reactions
• Emotional reactions
• The LDOS-CoMoDa Data
• Predictive Models Utilizing Emotional Reactions
• Findings and Results
7. How it works
7
Binary FeedbackRatings Reviews Behaviors
• User Preferences
Explicit Implicit
8. Non-context vs Context
8
Companion
User’s decision may vary from contexts to contexts
• Examples:
Travel destination: in winter vs in summer
Movie watching: with children vs with partner
Restaurant: quick lunch vs business dinner
Music: for workout vs for study
9. What is Context?
9
• “Context is any information that can be used to characterize
the situation of an entity” by Anind K. Dey, 2001
• Observed Context:
Contexts are those variables which may change when a same
activity is performed again and again.
10. What is Context?
10
Activity Structure:
1). Subjects: group of users
2). Objects: group of items/users
3). Actions: the interactions within the activities
Which variables could be context?
1). Attributes of the actions
Watching a movie: time, location, companion
Listening to a music: time, occasions, etc
2). Dynamic attributes or status from the subjects
User emotions
Yong Zheng. "A Revisit to The
Identification of Contexts in
Recommender Systems", IUI 2015
11. Emotion in RecSys
11
1). Emotions are helpful in personalization
2). Emotions can be considered as effective contexts in RecSys
12. Summary
12
We use emotions as context in recommender systems
• Recommender Systems
• Context-aware Recommender Systems
• What is Context?
Adapt to emotional reactions
• Emotional reactions
• The LDOS-CoMoDa Data
• Predictive Models Utilizing Emotional Reactions
• Findings and Results
14. Emotions in User Interactions
14
Tkalcic, Marko, Andrej Kosir, and Jurij Tasic. "Affective recommender systems: the role of
emotions in recommender systems." Proc. The RecSys 2011 Workshop on Human
Decision Making in Recommender Systems. 2011.
15. Emotions in User Interactions
15
Entry: before movie watching
Consumption: during movie watching
Exit: after movie watching, e.g., user post-ratings
17. Emotional Expression and Reactions
17
User may have similar rating behaviors but different
emotional expressions or reactions: WHY??????????
• Different Expectations and Outcomes
– Happy: Well, it is good movie!
– Sad: A sad story. I was moved by the movie
– Surprised: Better than I thought…
• Different User Personality in Emotional Expressions
H. S. Friedman and S. Booth-Kewley. Personality, type a behavior, and coronary heart disease: the
role of emotional expression. Journal of Personality and Social Psychology, 53(4):783, 1987.
L. Harker and D. Keltner. Expressions of positive emotion in women’s college yearbook pictures and
their relationship to personality and life outcomes across adulthood. Journal of personality and
social psychology, 80(1):112, 2001
18. The LDOS-CoMoDa Movie Rating Data Set
18
LDOS-CoMoDa data set, http://www.ldos.si/comoda.html
19. The LDOS-CoMoDa Movie Rating Data Set
19
Pre-Emotions
• Mood
Post-Emotions
• domEmo emotional state during the process
• endEmo final emotional state after the process
Emotional Reactions
• It’s defined as emotions in response to items or
user activities, i.e., expression of post-emotions
20. Utilize Emotional Reactions in CARS
20
Emotional Reactions
• Emotions in response to items or user activities
Assumptions:
• Users may have different (even reversed)
emotional reactions, but they may have similar
rating behaviors finally
• For example, user’s post-emotions may be
negative, but finally still leave positive ratings
21. Assumption Validation by Data
21
Post-emotion: negative + rating: positive
Post-emotion: positive + rating: negative
Unusual
case
23. Utilize Emotional Reactions in CARS Algorithm
23
Model-1: emotional regularization
• Emotional User = User + Post-Emotion
• Post-Emotion could be either domEmo or endEmo
• We measure the similarities between emo users
• We assume user’s rating deviation in corresponding
post-emotional state should be similar, if two
emotional users are similar
Context-aware Matrix Factorization
24. Utilize Emotional Reactions in CARS Algorithm
24
Model-1: emotional regularization
• We assume user’s rating deviation in corresponding
post-emotional state should be similar, if two
emotional users are similar
as weight
25. Utilize Emotional Reactions in CARS Algorithm
25
Model-2: emotion + user regularization
• In addition to similar emotional users, the original
users may be similar to some extent
as weight
26. Results and Findings
26
domEmo_B: model with emotional regularization only
domEmo_B,u: model with emotional and user regularizations
We chose domEmo and endEmo as post-emotion respectively
Findings
1) There are improvements
2) domEmo is more effective
27. Conclusions
27
• Assumptions: Users may have different (even
reversed) emotional reactions, but they may have
similar rating behaviors finally
• We validate this assumption in LDOS-CoMoDa data
and utilize it to build emotional regularization in
context-aware matrix factorization algorithms
• We demonstrate the improvements and discover
domEmo is more effective than endEmo
28. Future Work
28
• Emotional Transitions?
– In this work, we just focus on the emotional expressions
at the exit stage
– We did not take pre-emotions into account
• By taken pre-emotions into account
– We may build finer-grained models
– We can better find similar emotional users
– But we also have to deal with sparsity problems
29. Adapt to Emotional Reactions In Context-
aware Personalization
Yong Zheng
Illinois Institute of Technology
Chicago, IL, USA
The 4th Workshop on Emotions and Personality in
Personalized Systems (EMPIRE), September 16, 2016