Cost-aware travel tour recommendation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 983-991

  Ge, Yong ^a   Liu, Qi ^a,b   Xiong, Hui ^a   Tuzhilin, Alexander ^c   Chen, Jian ^d  

^a EEUU   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c NEW YORK UNIVERSITY   (United States)

^d TSINGHUA UNIVERSITY   (China)

Author keywords

Cost aware recommendation; Matrix factorization

Indexed keywords

COSTS; DATA MINING; DECISION MAKING; GAUSSIAN DISTRIBUTION;

COST-AWARE; COST-AWARE RECOMMENDATION; GAUSSIAN PRIORS; LATENT FACTOR MODELS; MATRIX FACTORIZATIONS; REAL TIME DECISION-MAKING; REAL-TIME DECISION MAKING; TRAVEL COSTS; TRAVEL TOURS; UNCERTAINTY;

FACTORIZATION;

EID: 80052687461     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020568     Document Type: Conference Paper

References (30)

1. R. P. Adams, G. E. Dahl, and I. Murray. Incorporating side information in probabilistic matrix factorization with gaussian processes. In Computing Research Repository - CORR, 2010.
2. G. Adomavicius and A. Tuzhilin. Towards the next generation of recommender systems: A survey of the state-of-the art and possible extensions. TKDE, 2005, 2005.
3. D. Agarwal and B. C. Chen. Regression-based latent factor models. In In KDD'09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 19-28, 2009.
4. L. Baltrunas, F. Ricci, and B. Ludwig. Context relevance assessment for recommender systems. In Proceedings of the 2011 International Conference on Intelligent User Interfaces, 13-16 February 2011, Palo Alto, CA, USA, 2011.
5. R. M. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In IEEE ICDM 2007, pages 43-52, 2007.
6. R. D. Burke. Hybrid web recommender systems. Lecture Notes in Computer Science, 4321:377-408, 2007.
7. F. Cena, L. Console, C. Gena, A. Goy, G. Levi, S. Modeo, and I. Torre. Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communication, 19(4):369-384, 2006. (Pubitemid 44853588)
8. L.-S. Chen, F.-H. Hsu, M.-C. Chen, and Y.-C. Hsu. Developing recommender systems with the consideration of product profitability for sellers. Information Sciences, 178(4):1032-1048, 2008. (Pubitemid 350186169)
9. A. Das, C. Mathieu, and D. Ricketts. Maximizing profit using recommender systems. In WWW, 2010.
10. M. Deshpande and G. Karypis. Item-based top-n recommendation. In ACM Transactions on Information Systems, pages 22(1):143-177, 2004.
11. Y. Ge, H. Xiong, A. Tuzhilin, K. Xiao, M. Gruteser, and M. J. Pazzani. An energy-efficient mobile recommender system. In KDD 2010.
12. Q. Gu, J. Zhou, and C. H. Q. Ding. Collaborative filtering weighted nonnegative matrix factorization incorporating user and item graphs. In SIAM SDM, pages 199-210, 2010.
13. Q. Hao, R. Cai, C. Wang, R. Xiao, J.-M. Yang, Y. Pang, and L. Zhang. Equip tourists with knowledge mined from travelogues. In the 19th International World Wide Web Conference, 2010.
14. T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems - TOIS, 22(1):89-115, 2004.
15. K. Hosanagar, R. Krishnan, and L. Ma. Recommended for you: The impact of profit incentives on the relevance of online recommendations. In Proceedings of the International Conference on Information Systems, 2008.
16. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD 2008, pages 426-434, 2008.
17. Y. Koren. Collaborative filtering with temporal dynamics. In KDD 2009, pages 447-456, 2009.
18. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer - COMPUTER, 42(8):30-37, 2009.
19. L. Xiong, X. Chen, T. K. Huang, J. Schneider, J. G. Carbonell. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In SIAM International Conference on Data Mining, pages 211-222, 2010.
20. Q. Liu, E. Chen, H. Xiong, and C. H. Q. Ding. Exploiting user interests for collaborative filtering: interests expansion via personalized ranking. In ACM CIKM, pages 1697-1700, Toronto, Canada, 2010.
21. Z. Lu, D. Agarwal, and I. S. Dhillon. A spatio-temporal approach to collaborative filtering. In Conference on Recommender Systems - RecSys, pages 13-20, 2009.
22. H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In Research and Development in Information Retrieval, pages 203-210, 2009.
23. B. Marlin. Modeling user rating profiles for collaborative filtering. In In NIPS. MIT Press, 2003.
24. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Neural Information Processing Systems 21 (NIPS 2008), 2008.
25. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system - a case study. In In ACM WebKDD Workshop, 2000.
26. N. Srebro, J. Rennie, and T. Jaakkola. Maximum margin matrix factorizations. In Advances in Neural Information Processing Systems (NIPS) 17, 2005.
27. M. Wang, X.-S. Hua, J. Tang, and R. Hong. Beyond distance measurement: Constructing neighborhood similarity for video annotation. In IEEE Transactions on Multimedia, volume 11, 2009.
28. M. Wang, X.-S. Hua, J. Tang, and R. Hong. Unified video annotation via multi-graph learning. In IEEE Transactions on Circuits and Systems for Video Technology, volume 19, 2009.
29. G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. In In Proc. of SIGIR, pages 114-121, 2005.
30. Z. Yu, X. Zhou, D. Zhang, C.-Y. Chin, X. Wang, and J. Men. Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing, 5(3):68-75, July 2006.