Exploration / exploitation trade-off in mobile context-aware recommender systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7691 LNAI, Issue , 2012, Pages 591-601

  Bouneffouf, Djallel ^a   Bouzeghoub, Amel ^a   Gançarski, Alda Lopes ^a  

^a INSTITUT TELECOM   (France)

Author keywords

artificial intelligence; exploration exploitation dilemma; machine learning; recommender system

Indexed keywords

CONTEXT-AWARE RECOMMENDER SYSTEMS; CONTEXTUAL BANDITTI; EXPLORATION/EXPLOITATION DILEMMAS; LOG DATA; OFF-LINE SIMULATIONS; ON DYNAMICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

RECOMMENDER SYSTEMS;

EID: 84871391272     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-35101-3_50     Document Type: Conference Paper

References (23)

1. Alexandre, S., Moira Norrie, C., Grossniklaus, M., Signer, B.: Spatio-Temporal Proximity as a Basis for Collaborative Filtering in Mobile Environments. In: CAiSE (2006)
2. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite Time Analysis of the Multiarmed Bandit Problem. Machine Learning 47, 235-256 (2002)
3. Bader, R., Neufeld, E., Woerndl, W., Prinz, V.: Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods. In: Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, USA, pp. 23-30 (2011)
4. Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5) (2011)
5. Bellotti, V., Begole, B., Chi, E.H., Ducheneaut, N., Fang, J., Isaacs, E.: Activity-Based Serendipitous Recommendations. In: Proceedings on the Move, pp. 1157-1166. ACM, USA (2008)
6. Bouneffouf, D., Bouzeghoub, A., Gançarski, A.L.: Following the User's Interests in Mobile Context-Aware recommender systems. In: AINA Workshops, pp. 657-662. IEEE Computer Society, USA (2012)
7. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-Aware Recommender Systems. AI Magazine 32(3), 67-80 (2011)
8. Chu, W., Park, S.: Personalized recommendation on dynamic content using predictive bilinear models. In: Proc. of World Wide Web, pp. 691-700. ACM, Spain (2009)
9. Even-Dar, E., Mannor, S., Mansour, Y.: PAC Bounds for Multi-armed Bandit and Markov Decision Processes. In: Kivinen, J., Sloan, R.H. (eds.) COLT 2002. LNCS (LNAI), vol. 2375, pp. 255-270. Springer, Heidelberg (2002)
10. Kivinen, J., Manfred, K.W.: Exponentiated gradient versus gradient descent for linear predictors. In: Information and Computation, vol. 132, pp. 132-163. Academic Press, USA (1997)
11. Langford, J., Zhang, T.: The epoch-greedy algorithm for contextual multi-armed bandits. In: Advances in Neural Information Processing Systems, vol. 20, pp. 1096-1103. Curran Associates, Canada (2008)
12. Lakshmish, R., Deepak, P., Ramana, P., Kutila, G., Garg, D., Karthik, V., Shivkumar, K.: A Mobile context-aware, Social Recommender System for Low-End Mobile Device. In: Mobile Data Management: Systems, Services and Middleware CAESAR, pp. 338-347. IEEE Computer Society, USA (2009)
13. Lihong, L., Wei, C., Langford, J., Schapire, E.: A Contextual-Bandit Approach to Personalized News Document Recommendation. CoRR, Presented at the Nineteenth International Conference on World Wide Web, Raleigh, pp. 661-670. ACM, USA (2010)
14. Mannor, S., Tsitsiklis, J.: The Sample Complexity of Exploration in the Multi-Armed Bandit Problem. Computational Learning Theory 5, 623-648 (2004)
15. Mladenic, D.: Text-learning and related intelligent agents: A survey. IEEE Intelligent Agents 14, 44-54 (1999)
16. Robbins, H.: Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society (1952)
17. Samaras, G., Panayiotou, C.: Personalized portals for the wireless user based on mobile agents. In: Proc. 2nd Int'l Workshop on Mobile Commerce, pp. 70-74. ACM, USA (2002)
18. Schafer, J.B., Konstan, J.: Riedi. J.: Recommender systems in e-commerce. In: Proc. of the 1st ACM Conf. on Electronic Commerce, pp. 158-166. ACM, USA (1999)
19. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (1998)
20. Watkins, C.J.: Learning from Delayed Rewards. Ph.D. thesis. Cambridge University (1989)
21. Wei, L., Wang, X., Zhang, R., Cui, Y., Mao, J., Jin, R.: Exploitation and Exploration in a Performance based Contextual Advertising System. In: KDD, pp. 133-138. ACM, USA (2010)
22. Woerndl, W., Schulze, F.: Capturing, Analyzing and Utilizing Context-Based Information About User Activities on Smartphones. In: Activity Context Representation Techniques and Languages: Papers from the 2011 AAAI Workshop (2011)
23. Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133-138 (1994)