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[UMAP 2016] User-Oriented Context Suggestion

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YONG ZHENG
YONG ZHENG

User-Oriented Context Suggestion

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User-Oriented Context Suggestion Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence DePaul University, Chicago The 24th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, July 13-16, 2016
Question: Is it enough to have appropriate/good item recommendations?
Zoo Parks in San Diego, USA 3 • San Diego Zoo • San Diego Zoo Safari Park
Zoo Parks in San Diego, USA 4
Intro: Context Suggestion
Traditional Recommender Systems 6 • Task: Suggest a list of items to a user • For example, recommend me a list of movies to watch Traditional Rec
Context-aware Recommendation 7 • Task: Suggest a list of items to a user in specific contexts • For example, recommend me some movies to watch with my girlfriend at weekend in the cinema Contextual RecTraditional Rec
Context Suggestion 8 • Task: Suggest a list of contexts to users/items • For example: suggest me the time/location to watch a movie Context Rec Contextual RecTraditional Rec
What is Context? 9 • Our definition: Contexts are those variables which may change when a same activity is performed repeatedly – not only the time & location, but also companion, occasions, user intent/purpose, etc • Examples: Watching a movie: time, location, companion, etc Listening to a music: time, location, emotions, occasions, etc Party or Restaurant: time, location, occasion, etc Travels: time, location, weather, transportation condition, etc
Motivations
11 Context Suggestion: Motivations • Motivation-1: Maximize user experience User Experience (UX) refers to a person's emotions and attitudes about using a particular product, system or service.
12 Context Suggestion: Motivations • Motivation-1: Maximize user experience It is not enough to recommend good items only Good item recommendations cannot guarantee the whole user experience!
13 Context Suggestion: Motivations • Motivation-2: Contribute to Context Collection Predefine contexts and suggest them to users
14 Context Suggestion: Motivations • Motivation-3: Connect with Context-aware RecSys User’s actions on context is a context-query to system
Applications
16 Context Suggestion: Applications • There could be many potential applications:
17 Context Suggestion • There could be many applications, we focus on two tasks 1).UI-Oriented Context Suggestion Task: suggest contexts to <user, item> Example: time & location for me to watch Life of Pi Existing solutions: Multi-label classification/predictions 2). User-Oriented Context Suggestion Task: suggest contexts to each user Example: Google Music, Pandora, Youtube, etc Solution: this paper in UMAP 2016
Challenges and Solutions
19 Challenge: Evaluations 1).UI-Oriented Context Suggestion Task: suggest contexts to <user, item> Example: time & location for me to watch Life of Pi 2). User-Oriented Context Suggestion Task: suggest contexts to each user Example: Google Music, Pandora, Youtube, etc Same challenge: Evaluations!! We do not have user’s preferences on contexts. No data!
20 Evaluation: Solutions In this paper, we use a simulation-based approach. User’s taste on context conditions can be obtained by the average rating on context condition by users across contextual ratings over all rated items.
21 Algorithms: User-Oriented Context Suggestion Solution 1). By Contextual Rating Deviations (CRDs) CRD is used to tell how user’s rating is deviated in each context condition. For example, CRD(u, weekend) = 0.5, it tells that user u’s rating on items is usually higher by 0.5 if watching movies at weekend CAMF_C: CAMF_CU:
22 Algorithms: User-Oriented Context Suggestion Solution 2).By UI-Oriented Context Suggestion Color, Shape, Weight, Origin, Taste, Price, Vitamin c Predictions By UI-Oriented Context Suggestion Converted User-Context Predictions
23 Algorithms: User-Oriented Context Suggestion Solution 2).By UI-Oriented Context Suggestion We choose two methods in UI-Oriented context suggestion I). Multi-Label Classification (MLC) We use LabelPowerset (LP) + RandomForest II). Tensor Factorization (PITF) Color, Shape, Weight, Origin, Taste, Price, Vitamin c Users × Items × Contexts Ratings
Results and Findings We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation. 24 Simple Baseline By Context Rating Deviations By UI Context Suggestion
Results and Findings We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation. 25 Simple Baseline By Context Rating Deviations By UI Context Suggestion
Conclusions and Future Work • Conclusions PITF is the best algorithm; MLC is ranked the 2nd CRD-based approach works better than baseline • Future Work Collect appropriate data & Perform user studies Try other contextual recommendation algorithms • Acknowledgement  Student Travel Grant by US NSF 26
User-Oriented Context Suggestion Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence DePaul University, Chicago The 24th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, July 13-16, 2016

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[UMAP 2016] User-Oriented Context Suggestion

  • 1. User-Oriented Context Suggestion Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence DePaul University, Chicago The 24th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, July 13-16, 2016
  • 2. Question: Is it enough to have appropriate/good item recommendations?
  • 3. Zoo Parks in San Diego, USA 3 • San Diego Zoo • San Diego Zoo Safari Park
  • 4. Zoo Parks in San Diego, USA 4
  • 6. Traditional Recommender Systems 6 • Task: Suggest a list of items to a user • For example, recommend me a list of movies to watch Traditional Rec
  • 7. Context-aware Recommendation 7 • Task: Suggest a list of items to a user in specific contexts • For example, recommend me some movies to watch with my girlfriend at weekend in the cinema Contextual RecTraditional Rec
  • 8. Context Suggestion 8 • Task: Suggest a list of contexts to users/items • For example: suggest me the time/location to watch a movie Context Rec Contextual RecTraditional Rec
  • 9. What is Context? 9 • Our definition: Contexts are those variables which may change when a same activity is performed repeatedly – not only the time & location, but also companion, occasions, user intent/purpose, etc • Examples: Watching a movie: time, location, companion, etc Listening to a music: time, location, emotions, occasions, etc Party or Restaurant: time, location, occasion, etc Travels: time, location, weather, transportation condition, etc
  • 11. 11 Context Suggestion: Motivations • Motivation-1: Maximize user experience User Experience (UX) refers to a person's emotions and attitudes about using a particular product, system or service.
  • 12. 12 Context Suggestion: Motivations • Motivation-1: Maximize user experience It is not enough to recommend good items only Good item recommendations cannot guarantee the whole user experience!
  • 13. 13 Context Suggestion: Motivations • Motivation-2: Contribute to Context Collection Predefine contexts and suggest them to users
  • 14. 14 Context Suggestion: Motivations • Motivation-3: Connect with Context-aware RecSys User’s actions on context is a context-query to system
  • 16. 16 Context Suggestion: Applications • There could be many potential applications:
  • 17. 17 Context Suggestion • There could be many applications, we focus on two tasks 1).UI-Oriented Context Suggestion Task: suggest contexts to <user, item> Example: time & location for me to watch Life of Pi Existing solutions: Multi-label classification/predictions 2). User-Oriented Context Suggestion Task: suggest contexts to each user Example: Google Music, Pandora, Youtube, etc Solution: this paper in UMAP 2016
  • 19. 19 Challenge: Evaluations 1).UI-Oriented Context Suggestion Task: suggest contexts to <user, item> Example: time & location for me to watch Life of Pi 2). User-Oriented Context Suggestion Task: suggest contexts to each user Example: Google Music, Pandora, Youtube, etc Same challenge: Evaluations!! We do not have user’s preferences on contexts. No data!
  • 20. 20 Evaluation: Solutions In this paper, we use a simulation-based approach. User’s taste on context conditions can be obtained by the average rating on context condition by users across contextual ratings over all rated items.
  • 21. 21 Algorithms: User-Oriented Context Suggestion Solution 1). By Contextual Rating Deviations (CRDs) CRD is used to tell how user’s rating is deviated in each context condition. For example, CRD(u, weekend) = 0.5, it tells that user u’s rating on items is usually higher by 0.5 if watching movies at weekend CAMF_C: CAMF_CU:
  • 22. 22 Algorithms: User-Oriented Context Suggestion Solution 2).By UI-Oriented Context Suggestion Color, Shape, Weight, Origin, Taste, Price, Vitamin c Predictions By UI-Oriented Context Suggestion Converted User-Context Predictions
  • 23. 23 Algorithms: User-Oriented Context Suggestion Solution 2).By UI-Oriented Context Suggestion We choose two methods in UI-Oriented context suggestion I). Multi-Label Classification (MLC) We use LabelPowerset (LP) + RandomForest II). Tensor Factorization (PITF) Color, Shape, Weight, Origin, Taste, Price, Vitamin c Users × Items × Contexts Ratings
  • 24. Results and Findings We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation. 24 Simple Baseline By Context Rating Deviations By UI Context Suggestion
  • 25. Results and Findings We present the results based on the music data: 42 users, 139 items, 3938 ratings, 34 contexts to be suggested. We examine top-5 suggestions by the 5-fold cross validation. 25 Simple Baseline By Context Rating Deviations By UI Context Suggestion
  • 26. Conclusions and Future Work • Conclusions PITF is the best algorithm; MLC is ranked the 2nd CRD-based approach works better than baseline • Future Work Collect appropriate data & Perform user studies Try other contextual recommendation algorithms • Acknowledgement  Student Travel Grant by US NSF 26
  • 27. User-Oriented Context Suggestion Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence DePaul University, Chicago The 24th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, July 13-16, 2016