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[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies

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YONG ZHENG
YONG ZHENG

Proceedings of the 16th IEEE/WIC/ACM International Conference on Web Intelligence (WI'17), Leipzig, Germany, August 2017

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Context Suggestion: Empirical Evaluations vs User Studies Yong Zheng School of Applied Technology Illinois Institute of Technology Chicago, IL, 60616, USA The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI) August 23-26, 2017, Leipzig, Germany
Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 2
Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 3
Recommender System (RS) • RS: item recommendations tailored to user tastes 4
Context-Aware Recommendation 5 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
Terminology in CARS 6 • Example of Multi-dimensional Context-aware Data set ➢Context Dimension: time, location, companion ➢Context Condition: Weekend/Weekday, Home/Cinema ➢Context Situation: {Weekend, Home, Kids} User Item Rating Time Location Companion U1 T1 3 Weekend Home Kids U1 T2 5 Weekday Home Partner U2 T2 2 Weekend Cinema Partner U2 T3 3 Weekday Cinema Family U1 T3 ? Weekend Cinema Kids
What is Context? 7 The most common contextual variables: ➢Time and Location ➢User intent or purpose ➢User emotional states ➢Devices ➢Topics of interests, e.g., apple vs. Apple ➢Others: companion, weather, budget, etc Usually, the selection/definition of contexts is a domain-specific problem
Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 8
Motivations: Context-Drive Applications 1) Context is necessary to maximize the user experience. A list of good item recommendations is NOT enough. 2) It is difficult to collect context information on the Web!!!! Context suggestion provides a way for context acquisition 9
Motivation: User Experience 10 • San Diego Zoo • San Diego Zoo Safari Park
Motivation: User Experience 11
Motivation: User Experience 12
13 Motivation: Context Acquisition • Google Music
Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 14
Solution for Context Suggestion • Direct Context Prediction The output is a binary prediction The value “1” indicates appropriate suggestion The value “0” tells inappropriate suggestion • Indirect Context Suggestion The output is a top-N recommendations Task: Suggest appropriate contexts for a user to enjoy a given item 15
Direct Context Prediction 16 Treat context conditions as binary labels Utilize multi-label classification as solution
Indirect Context Suggestion 17 • Context-aware Recommendation Task: Given a user and context info Recommend a list of items
Indirect Context Suggestion 18 • Indirect Context Suggestion Task: Given a user and an item Recommend a list of appropriate contexts for the users to enjoy the items Item-Aware Context Recommendation
Research Problems • Direct Context Prediction was explored in WI’14, but Indirect Context Suggestion was never discussed and compared with the direct context prediction • Previous research infers user preferences on contexts from contextual ratings on the items – it is not validated that whether contextual ratings can tell whether a user prefers a given contexts to enjoy the items • There are no evaluation standards for context suggestion 19
Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 20
Data Collection • It’s first time to collect user’s tastes on contexts • 5043 ratings by 97 users on 79 movies 21
Experimental Settings • 5-fold Cross Validation • Direct Context Prediction – Classification Chains (MLC_CC) – Label Powerset (MLC_LP) • Indirect Context Suggestion – Tensor Factorization (TF) – Context-aware Matrix Factorization (CAMF) – Contextual Sparse Linear Methods (CSLIM) 22
Evaluation Mechanisms We propose the evaluation standards for context suggestion • Top-N Context Prediction – N varies from 1 to the number of context conditions – Any available N value is fine – It does not matter if two contexts from a same variable are suggested. For example, {weekend, weekday, home, kids} • Exact Context Suggestion – Top-N, but N = the number of context condition – For each dimension, we only suggest one condition For example, {weekend, home, kids} 23
Results (Top-N Suggestion) 24 By using contextual ratings as ground truth for evaluation purpose
Results (Top-N Suggestion) 25 By using user tastes on contexts (from user surveys) as ground truth
Results (Exact Suggestion) 26
Results (Exact Suggestion) 27
Conclusions and Findings 28 • “General” indicates user’s general preferences on contexts for movie watching, without considering which movie it is • Personalization is required, since many algorithms outperform the “General” method • UISplitting and TF are the best ones • Indirect context suggestion is better to offer better suggestions than direct context prediction • The results by using contextual ratings and user tastes on contexts are consistent.
Future Work 29 • We will try to collect more data and evaluate these solutions on larger data set • We will try to utilize the context suggestion methods to predict emotional states • We will seek solutions to improve the indirect context prediction
Context Suggestion: Empirical Evaluations vs User Studies Yong Zheng School of Applied Technology Illinois Institute of Technology Chicago, IL, 60616, USA The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI) August 23-26, 2017, Leipzig, Germany

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[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies

  • 1. Context Suggestion: Empirical Evaluations vs User Studies Yong Zheng School of Applied Technology Illinois Institute of Technology Chicago, IL, 60616, USA The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI) August 23-26, 2017, Leipzig, Germany
  • 2. Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 2
  • 3. Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 3
  • 4. Recommender System (RS) • RS: item recommendations tailored to user tastes 4
  • 5. Context-Aware Recommendation 5 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
  • 6. Terminology in CARS 6 • Example of Multi-dimensional Context-aware Data set ➢Context Dimension: time, location, companion ➢Context Condition: Weekend/Weekday, Home/Cinema ➢Context Situation: {Weekend, Home, Kids} User Item Rating Time Location Companion U1 T1 3 Weekend Home Kids U1 T2 5 Weekday Home Partner U2 T2 2 Weekend Cinema Partner U2 T3 3 Weekday Cinema Family U1 T3 ? Weekend Cinema Kids
  • 7. What is Context? 7 The most common contextual variables: ➢Time and Location ➢User intent or purpose ➢User emotional states ➢Devices ➢Topics of interests, e.g., apple vs. Apple ➢Others: companion, weather, budget, etc Usually, the selection/definition of contexts is a domain-specific problem
  • 8. Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 8
  • 9. Motivations: Context-Drive Applications 1) Context is necessary to maximize the user experience. A list of good item recommendations is NOT enough. 2) It is difficult to collect context information on the Web!!!! Context suggestion provides a way for context acquisition 9
  • 10. Motivation: User Experience 10 • San Diego Zoo • San Diego Zoo Safari Park
  • 14. Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 14
  • 15. Solution for Context Suggestion • Direct Context Prediction The output is a binary prediction The value “1” indicates appropriate suggestion The value “0” tells inappropriate suggestion • Indirect Context Suggestion The output is a top-N recommendations Task: Suggest appropriate contexts for a user to enjoy a given item 15
  • 16. Direct Context Prediction 16 Treat context conditions as binary labels Utilize multi-label classification as solution
  • 17. Indirect Context Suggestion 17 • Context-aware Recommendation Task: Given a user and context info Recommend a list of items
  • 18. Indirect Context Suggestion 18 • Indirect Context Suggestion Task: Given a user and an item Recommend a list of appropriate contexts for the users to enjoy the items Item-Aware Context Recommendation
  • 19. Research Problems • Direct Context Prediction was explored in WI’14, but Indirect Context Suggestion was never discussed and compared with the direct context prediction • Previous research infers user preferences on contexts from contextual ratings on the items – it is not validated that whether contextual ratings can tell whether a user prefers a given contexts to enjoy the items • There are no evaluation standards for context suggestion 19
  • 20. Agenda • Intro: Context-aware Recommender Systems • Motivations: Context Suggestion • Methodologies and Research Problems – Direct Context Prediction – Indirect Context Suggestion • Experimental Results and Findings • Conclusions and Future Work 20
  • 21. Data Collection • It’s first time to collect user’s tastes on contexts • 5043 ratings by 97 users on 79 movies 21
  • 22. Experimental Settings • 5-fold Cross Validation • Direct Context Prediction – Classification Chains (MLC_CC) – Label Powerset (MLC_LP) • Indirect Context Suggestion – Tensor Factorization (TF) – Context-aware Matrix Factorization (CAMF) – Contextual Sparse Linear Methods (CSLIM) 22
  • 23. Evaluation Mechanisms We propose the evaluation standards for context suggestion • Top-N Context Prediction – N varies from 1 to the number of context conditions – Any available N value is fine – It does not matter if two contexts from a same variable are suggested. For example, {weekend, weekday, home, kids} • Exact Context Suggestion – Top-N, but N = the number of context condition – For each dimension, we only suggest one condition For example, {weekend, home, kids} 23
  • 24. Results (Top-N Suggestion) 24 By using contextual ratings as ground truth for evaluation purpose
  • 25. Results (Top-N Suggestion) 25 By using user tastes on contexts (from user surveys) as ground truth
  • 28. Conclusions and Findings 28 • “General” indicates user’s general preferences on contexts for movie watching, without considering which movie it is • Personalization is required, since many algorithms outperform the “General” method • UISplitting and TF are the best ones • Indirect context suggestion is better to offer better suggestions than direct context prediction • The results by using contextual ratings and user tastes on contexts are consistent.
  • 29. Future Work 29 • We will try to collect more data and evaluate these solutions on larger data set • We will try to utilize the context suggestion methods to predict emotional states • We will seek solutions to improve the indirect context prediction
  • 30. Context Suggestion: Empirical Evaluations vs User Studies Yong Zheng School of Applied Technology Illinois Institute of Technology Chicago, IL, 60616, USA The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI) August 23-26, 2017, Leipzig, Germany