Collaborative filtering on streaming data with interest-drifting

Intelligent Data Analysis

Volumn 11, Issue 1, 2007, Pages 75-87

  Li, Xue ^a   Barajas, Jorge M ^a   Ding, Yi ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38349041037     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2007-11105     Document Type: Article

References (40)

1. P. Resnick and H.R. Varian, Recommender systems, Commun ACM 40 (1997), 56-58.
2. D. Goldberg, D. Nichols, B.M. Oki and D. Terry, Using collaborative filtering to weave an information tapestry, Commun ACM 35 (1992), 61-70.
3. U. Shardanand and P. Maes, Social information filtering: algorithms for automating "word of mouth", In: CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems, New York, NY, USA, ACM Press/Addison-Wesley Publishing Co. (1995), 210-217.
4. D. Oard and J. Kim, Implicit feedback for recommender systems, in: Implicit Feedback for Recommender Systems, D. Oard and J. Kim, eds, In Proceedings of the AAAI Workshop on Recommender Systems, July 1998.
5. D. Nichols, Implicit Rating and Filtering, In: Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering, ERCIM, 1998, 31-36.
6. J.S. Breese, D. Heckerman and C. Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, In: Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 1998, 43-52.
7. B.M. Sarwar, G. Karypis, J.A. Konstan and J. Reidl, Item-Based Collaborative Filtering Recommendation Algorithms, In: World Wide Web, 2001, 285-295.
8. P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm and J. Riedl, GroupLens: An Open Architecture for Collaborative Filtering of Netnews, In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, North Carolina, ACM, 1994, 175-186.
9. T. Hofmann, Collaborative Filtering Via Gaussian Probabilistic Latent Semantic Analysis, In: SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, New York, NY, USA, ACM Press, 2003, 259-266.
10. C.N. Hsu, H.H. Chung and H.S. Huang, The Hybrid Poisson Aspect Model for Personalized Shopping Recommendation, In: Third IEEE International Conference on Data Mining, 2003, 545-548.
11. Z. Huang, H. Chen and D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Trans Inf Syst 22 (2004), 116-142.
12. A.I. Schein, A. Popescul, L.H. Ungar and D.M. Pennock, Methods and Metrics for Cold-Start Recommendations, In: SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, ACM Press, 2002, 253-260.
13. Y. Ding and X. Li, Time Weight Collaborative Filtering, In: CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, New York, NY, USA, ACM Press, 2005, 485-492.
14. J. Schlimmer and R. Granger, Beyond Incremental Processing: Tracking Concept Drift, In: Proceedings of the 5th National Conference on Artificial Intelligence, 1986, 502-507.
15. J. Basilico and T. Hofmann, A Joint Framework for Collaborative and Content Filtering, In: SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, ACM Press, 2004, 550-551.
16. L. Si and R. Jin, Unified Filtering by Combining Collaborative Filtering and Content-Based Filtering via Mixture Model and Exponential Model, In: CIKM '04: Proceedings of the Thirteenth ACM conference on Information and knowledge management, Washington, D.C., USA, 2004, 156-157.
17. N. Good, J. Schafer, J. Konstan, J. Borchers, B. Sarwar, J. Herlocker and J. Riedl, Combining Collaborative Filtering with Personal Agents for Better Recommendations, In: AAAI99: Proceedings of the 1999 Conference of the American Association of Artificial Intelligence. 1999, 439-446.
18. B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Item-Based Collaborative Filtering Recommendation Algorithms, In: International World Wide Web Conference, 2001, 285-295.
19. G.R. Xue, C. Lin, Q. Yang, W. Xi, H.J. Zeng, Y. Yu and Z. Chen, Scalable Collaborative Filtering using Cluster-Based Smoothing, In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, Salvador, Brazil, 2005, 114-121.
20. G. Linden, B. Smith and J. York, Industry report: Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Distributed Systems Online 4 (2003).
21. M.L. Calderón-Benavides, C.N. González-Caro, J. de J. Pérez-Alcázar, J.C. García-Díaz and J. Delgado, A Comparison of Several Predictive Algorithms for Collaborative Filtering on Multi-valued Ratings, In: SAC '04: Proceedings of the 2004 ACM symposium on Applied computing, New York, NY, USA, ACM Press, 2004, 1033-1039.
22. A. Nakamura and N. Abe, Collaborative Filtering using Weighted Majority Prediction Algorithms, In: ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., 1998, 395-403.
23. J. Delgado and N. Ishii, Memory-Based Weighted-Majority Prediction for Recommender Systems, In: Proceedings of the ACM SIGIR-99, 1999.
24. T. Tang, P. Winoto and C. K.C., On the Temporal Analysis for Improved Hybrid Recommendations, In: Web Intelligence, 2003. WI2003. Proceedings. IEEE/WIC International Conference on, 2003, 214-220.
25. L. Terveen, J. McMackin, B. Amento and W. Hill, Specifying Preferences Based on user History, In: Conference on Human Factors in Computing Systems, Minneapolis, Minnesota, USA, 2002, 315-322.
26. K. Sugiyama, K. Hatano and M. Yoshikawa, Adaptive Web Search Based on User Profile Constructed Without Any Effort from Users, In: Proceedings of the 13th international conference on World Wide Web, (New York, NY, USA), 2004, 675-684.
27. Y. Zhao, C. Zhang an S. Zhang, A Recent-Biased Dimension Reduction Technique for Time Series Data, In: PAKDD 2005: Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference, Lecture Notes in Computer Science, 2005.
28. C.N. Ziegler, G. Lausen and L. Schmidt-Thieme, Taxonomy-Driven Computation of Product Recommendations, In: CIKM '04: Proceedings of the Thirteenth ACM conference on Information and knowledge management, New York, NY, USA, ACM Press, 2004, 406-415.
29. P. Ganesan, H. Garcia-Molina and J. Widom, Exploiting hierarchical domain structure to compute similarity, ACM Trans InfSyst 21 (2003), 64-93.
30. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993.
31. C. Basu, H. Hirsh and W.W. Cohen, Recommendation as Classification: Using Social and Content-Based Information in Recommendation, In: AAAI/IAAI, 1998, 714-720.
32. P. Domingos and G. Hulten, Mining High-Speed Data Streams, In: Knowledge Discovery and Data Mining, 2000, 71-80.
33. J. Gama, R. Rocha and P. Medas, Accurate Decision Trees for Mining Highspeed Data Streams, In: SIGKDD03: 9th ACM International Conference on Knowledge Discovery and Data Mining, 2003, 523-528.
34. R. Jin and G. Agrawal, Efficient Decision Tree Construction on Streaming Data, In: SIGKDD03: 9th ACM International Conference on Knowledge Discovery and Data Mining, 2003, 571-576.
35. G. Hulten, L. Spencer and P. Domingos, Mining Time-Changing Data Streams, In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2001, 97-106.
36. G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning (1996), 69-101.
37. V. Ganti, J. Gehrke and R.R. Demon, Mining and monitoring evolving data, TKDE: IEEE Transactions on Knowledge and Data Engineering (2001), 50-63.
38. H. Wang, W. Fan, P. Yu and J. Han, Mining Concept-Drifting Data Streams using Ensemble Classifiers, In: SIGKDD03: 9th ACM International Conference on Knowledge Discovery and Data Mining, 2003.
39. P.M. Long, The complexity of learning according to two models of a drifting environment, Machine Learning (1999), 337-354.
40. P.L. Bartlett, S. David and S. Kulkarni, Learning changing concepts by exploiting the structure of change, Machine Learning (2000), 153-174.