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[WI 2014]Context Recommendation Using Multi-label Classification

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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.

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[WI 2014]Context Recommendation Using Multi-label Classification

  1. 1. Context Recommendation Using Multi-label Classification Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence, DePaul University, Chicago IEEE/WIC/ACM Conference on Web Intelligence Aug 14, Warsaw, Poland
  2. 2. Intro – Recommender Systems • Information Overload Problem  IR and RS • Recommender systems (RS) are the systems being able to provide recommendations to the end users. Currently, RS are popular everywhere: • E-Commerce: Amazon, Ebay, Newegg, etc • Social networks: Twitter, Facebook, etc • Movie/Stream: Netflix, Movie Pilot, Youtube, etc • Music: Pandora, Last.FM, etc
  3. 3. Intro – Type of Recommendations • [Item Recommendations] • [User Recommendations]
  4. 4. Intro – Context-aware Recommender • Context-aware Recommender Systems (CARS) CARS is a new type of RS which provide recommendations by adapting to users’ contextual situations. • Traditional RS: Users × Items  Ratings • Contextual RS: Users × Items × Contexts Ratings • Assumptions behind: 1). User may have different preferences in different contexts; 2). Contexts are important in decision-makings. However, what CARS recommended are still items or users. Companion
  5. 5. New Application: Context Recommender • In this paper, we propose a new application: context recommender (CR), which is able to recommend or suggest appropriate contexts for users to select or consume items. • Sample of contexts: time, location, companion, etc Context Rec User RecItem Rec
  6. 6. Possible Examples (Amazon.com) Note: They are not REAL applications/examples right now.
  7. 7. Possible Examples (IMDB.com)
  8. 8. Related Work L. Baltrunas, et al. "Best usage context predictions for music tracks", The 2nd Workshop on Context-aware Recommender Systems, ACM RecSys, 2010 This is the only work related to context recommendation, where the authors tried to provide suggestions of appropriate contexts to listen to music tracks. Pros: they proposed three KNN-based classifiers to suggest appropriate contexts; Cons: they proposed a specific application, but not a general delineation of the problem of context recommendations.
  9. 9. Contributions: Context Recommender • We formally provide the definition of CR • We propose the formal framework of CR applications • We discuss the algorithmic paradigms for CR • We examine the algorithms using multi-label classifications
  10. 10. Context Recommender 1. Definitions Context recommenders are the systems being able to recommend or suggest appropriate contexts for users to select or consume items. Examples: When is the best season to travel to Poland for user Tom? Who is the suggested companion to see “Titanic” with Tom? This book is better to be gifted to Mom or Kids? 2. Research Problems How to infer the appropriate contexts? And those contexts should be personalized or not? Example: The best season to Poland is always the same for all the users, or it could be personalized for different specific users? E.g. Tom likes winter much more than summer, where most users prefer summers in Poland.
  11. 11. Context Recommender 3. Context Recommendations App • There could be many other types, for example, it could be a group of users or items, instead of a single user or item. E.g. what is the best season to Poland by this group of travelers (e.g. Tour Group, etc), where the suggested contexts should meet the requirement of the group of users, instead of a single user. • In this paper, we focus on the general form: {User, Item}  Contexts; a pair of <user, item> as input Input Output App {User, Item} Contexts The best season to Poland for Tom {User} Contexts The best travel season for Tom {Item} Contexts The best travel season to Poland
  12. 12. Context Recommender 3. Algorithmic Paradigms What are the possible algorithms to recommend contexts? After analysis, we propose two series of frameworks: 1). Direct Context Prediction We infer suggested contexts by <user, item, preferences>; In other words, contexts are predicted based on users’ previous preference histories associated with <item, contexts> 2). Indirect Context Recommendation We reuse the context-aware recommendation algorithms: In CARS, Therefore, we vary the choices of contexts, and finally recommend the contexts which can contribute to the best ratings user will give to the item.
  13. 13. Context Recommender 3. Algorithmic Paradigms Which algorithms can be applied in each category? 1). Direct Context Prediction Classification algorithms are the popular ones which are applied to this category, where they have been adopted in context predictions in the pervasive computing area. 2). Indirect Context Recommendation We reuse the context-aware recommendation algorithms; Therefore, all available CARS algorithms can be applied to. However, the drawback of this category is the high computational cost if there are too many contextual conditions in the data. And it also relies on how the CARS algorithms perform. In this paper, we focus on Direct Context Prediction using multi-label classification algorithms.
  14. 14. Context Recommender 4. Why Multi-label Classifications (MLC)? 1).Binary Classification (is this an apple?) 2).Multi-class Classification (is it an apple|orange|pear?) 3).Multi-label Classification (<round, apple, fruit, Mac>) In other words, MLC allows the system to select more than 1 labels from the set. Classification is used for context predictions; and MLC just fits the requirement of context recommendation task.
  15. 15. Context Recommender 4. Why Multi-label Classifications (MLC)? Two series of MLC algorithms: 1). Transformation Algorithms They can use traditional classification algorithms (e.g. decision trees, SVM, etc), and they transform the MLC task to multiple binary or multi-class classification tasks. So they do not need to develop new algorithms. E.g., binary relevance (BR), label powerset (LP), classifier chains (CC), k-labelsets (RAkEL) 2). Adaptation Algorithms Develop new classification algorithms to adapt to the MLC task. E.g., Binary relevance KNN (BRKNN), Multi-label KNN (MLKNN)
  16. 16. Context Recommender 5. Experiments and Evaluations (Algorithms) Toolkit: Mulan (MLC toolkit) and Weka Java-based library MLC algorithms: BR, LP, CC, RAkEL, MLKNN, BRKNN; Classification methods used in BR, LP, CC and RAkEL: KNNclassifier (KNN), decision trees (J48), naive bayes (NB),Bayesian nets (BN) and support vector machine (SMO) Baseline algorithms: 1).the three KNN classifiers: RatingBased (RB), BestContextVectorBased (BCVB) and BestContextBased (BCB) proposed by L. Baltrunas, et al 2).Non-personalized methods: such as most popular algs Data # of users # of items # of ratings # of labels Rating scale AdomMovie 69 176 1,010 8 1-13 LDOS 113 1186 2,094 17 1-5 TripAdvisor 2731 2269 14,175 5 1-5
  17. 17. Context Recommender 5. Experiments and Evaluations (Metrics) Inputs: User, Item, Binary Preference (Good or Bad) Outputs: A list of predicted contexts Time = Weekend Time = Weekday Companion = Kids Companion = Parents Companion = Girlfriend Real 0 1 0 0 1 Prediction 1 0 0 0 1 Y is the set of TRUE labels in the ground truth, and Z is the set of predicted TRUE labels. m = # of examples. Another metric is hamming loss which measures the average percentage of incorrectly predicted labels.
  18. 18. Context Recommender 6. Experimental Results Due to limited time, we only present results on LDOS data.
  19. 19. Context Recommender 5. Experimental Results (findings) 1).Personalization is required, because personalized algorithms work much better than the non-personalized ones. i.e., simply recommending most popular ones (e.g. most popular season people visiting Poland) is not enough; 2).LP algorithm using SMO as classifier is the best MLC algorithms among all data sets and all methods examined. They beat all the other algorithms. KNN-based approaches worked bad, because context-aware data are usually sparse. 3).SMO is the best classifier used for MLC algorithms, but it increases computation costs if data is large and there are many contextual labels. The alternative choice is Bayesian Nets which worked good and not time-consuming 4). About running performance: LP using SMO is the best choice, but both LP and SMO increase computation costs if data is large and there are many contextual labels. Solutions: a). Reduce the number of labels by pre-selections; b). Choose LP using Bayesian Nets
  20. 20. Conclusions and Future Work • We formally introduce and discuss the application and research problem of context recommendations (CR). We believe that context recommenders will provide many more novel applications and new recommendation opportunities for both practical use and the research community. • We propose the formal framework of CR applications and discuss the algorithmic paradigms for CR. • We examine the algorithms using multi-label classifications, and infer some significant findings and patterns as introduced previously. • Future work: Examine more other algorithms, and develop new evaluation metrics for this domain.
  21. 21. References • Context Recommendations [1].Baltrunas, Linas, Marius Kaminskas, Francesco Ricci, Lior Rokach, Bracha Shapira, and Karl-Heinz Luke. "Best usage context prediction for music tracks." In Proceedings of the 2nd Workshop on Context Aware Recommender Systems. 2010. [2].Yong Zheng, Bamshad Mobasher, Robin Burke. "Context Recommendation Using Multi-label Classification". In Proceedings of the 13th IEEE/WIC/ACM International Conference on Web Intelligence, 2014 • Multi-label Classifications [1].Tsoumakas, Grigorios, and Ioannis Katakis. "Multi-label classification: An overview." International Journal of Data Warehousing and Mining (IJDWM) 3, no. 3 (2007): 1-13. [2].Tsoumakas, Grigorios, Ioannis Katakis, and Ioannis Vlahavas. "Mining multi-label data." In Data mining and knowledge discovery handbook, pp. 667-685. Springer US, 2010.
  22. 22. Thanks! Yong Zheng, Bamshad Mobasher, Robin Burke Center for Web Intelligence, DePaul University, Chicago IEEE/WIC/ACM Conference on Web Intelligence Aug 14, Warsaw, Poland

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