ABSTRACT
Recommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the recommendation performance. The development of context-aware recommender systems brings a new opportunity - context suggestion which refers to the task of recommending appropriate contexts to the users to improve user experience. In this paper, we explore the question whether user's contextual ratings can be reused to produce context suggestions. We propose two evaluation mechanisms for context suggestion, and empirically compare direct context predictions and indirect context suggestions based on a movie data that was collected from user studies. The experimental results reveal that indirect context suggestion works better than the direct context prediction, and tensor factorization is the best approach to produce context suggestions in our movie data.
- Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. AI Magazine 32, 3 (2011), 67--80.Google ScholarDigital Library
- Linas Baltrunas, Marius Kaminskas, Francesco Ricci, Rokach Lior, Bracha Shapira, and Karl-Heinz Luke. 2010. Best usage context predictions for music tracks. In The 2nd Workshop on Context-aware Recommender Systems.Google Scholar
- Linas Baltrunas, Bernd Ludwig, and Francesco Ricci. 2011. Matrix factorization techniques for context aware recommendation. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 301--304. Google ScholarDigital Library
- Linas Baltrunas and Francesco Ricci. 2009. Context-based splitting of item ratings in collaborative filtering. In Proceedings of ACM conference on Recommender systems. 245--248. Google ScholarDigital Library
- Klaus Brinker, Johannes Fürnkranz, and Eyke Hüllermeier. 2006. A unified model for multilabel classification and ranking. In Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29-September 1, 2006, Riva del Garda, Italy. IOS Press, 489--493. Google ScholarDigital Library
- Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 79--86. Google ScholarDigital Library
- Chihiro Ono, Mori Kurokawa, Yoichi Motomura, and Hideki Asoh. 2007. A context-aware movie preference model using a Bayesian network for recommendation and promotion. In International Conference on User Modeling. Springer, 247--257. Google ScholarDigital Library
- Chihiro Ono, Yasuhiro Takishima, Yoichi Motomura, and Hideki Asoh. 2009. Context-aware preference model based on a study of difference between real and supposed situation data. (2009), 102--113. Google ScholarDigital Library
- Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. 2009. Classifier chains for multi-label classification. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 254--269.Google ScholarCross Ref
- Alan Said, Ernesto W De Luca, and Sahin Albayrak. 2011. Inferring contextual user profiles - improving recommender performance. In ACM RecSys, the 4th Workshop on Context-Aware Recommender Systems.Google Scholar
- Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2009. Mining multi-label data. In Data mining and knowledge discovery handbook. Springer, 667--685.Google Scholar
- Grigorios Tsoumakas and Ioannis Vlahavas. 2007. Random k-labelsets: An ensemble method for multilabel classification. In European Conference on Machine Learning. Springer, 406--417. Google ScholarDigital Library
- Shankar Vembu and Thomas Gärtner. 2010. Label ranking algorithms: A survey. In Preference learning. Springer, 45--64.Google Scholar
- Yong Zheng. 2015. Context Suggestion: Solutions and Challenges. In Proceedings of the 15th IEEE International Conference on Data Mining Workshops. IEEE, 1602--1603. Google ScholarDigital Library
- Yong Zheng. 2015. A revisit to the identification of contexts in recommender systems. In Proceedings of the Conference on Intelligent User Interfaces Companion. ACM, 133--136. Google ScholarDigital Library
- Yong Zheng. 2017. Affective Prediction By Collaborative Chains In Movie Recommendation. In Proceedings of the 2017 IEEE/WIC/ACM International Conferences on Web Intelligence. IEEE. Google ScholarDigital Library
- Yong Zheng. 2017. Criteria Chains: A Novel Multi-Criteria Recommendation Approach. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM, 29--33. Google ScholarDigital Library
- Yong Zheng. 2017. Indirect Context Suggestion. In Proceedings of the 2017 Conference on User Modeling Adaptation and Personalization. ACM. Google ScholarDigital Library
- Yong Zheng. 2017. Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts. In Proceedings of the ACM Symposium on Applied Computing. ACM, 689--692. Google ScholarDigital Library
- Yong Zheng, Robin Burke, and Bamshad Mobasher. 2014. Splitting approaches for context-aware recommendation: An empirical study. In Proceedings of the 29th Annual ACM Symposium on Applied Computing. ACM, 274--279. Google ScholarDigital Library
- Y. Zheng, B. Mobasher, and R. Burke. 2014. Context Recommendation Using Multi-label Classification. In Proceedings of the 13th IEEE/WIC/ACM International Conference on Web Intelligence. IEEE, 288--295. Google ScholarDigital Library
- Y. Zheng, B. Mobasher, and R. Burke. 2014. CSLIM: Contextual SLIM Recommendation Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 301--304. Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2015. CARSkit: A java-based context-aware recommendation engine. In Data Mining Workshop (ICDMW), 2015 IEEE International Conference on. IEEE, 1668--1671. Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2015. Integrating Context Similarity with Sparse Linear Recommendation Model. In Proceedings of the 2015 Conference on User Modeling Adaptation and Personalization. Springer Berlin Heidelberg, 370--376.Google ScholarCross Ref
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2015. Similarity-Based Context-aware Recommendation. In Proceedings of the 2015 Conference on Web Information Systems Engineering. Springer Berlin Heidelberg, 431--447. Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2016. User-Oriented Context Suggestion. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. ACM, 249--258. Google ScholarDigital Library
Index Terms
- Context suggestion: empirical evaluations vs user studies
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