A new approach for combining content-based and collaborative filters

Journal of Intelligent Information Systems

Volumn 27, Issue 1, 2006, Pages 79-91

  Kim, Byeong Man ^a   Li, Qing ^a,b   Park, Chang Seok ^a   Kim, Si Gwan ^a   Kim, Ju Yeon ^c  

^a Kumoh National Institute of Technology   (South Korea)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^c Bucheon College   (South Korea)

Author keywords

Collaborative filtering; Content based filtering; Information filtering; Recommendation system

Indexed keywords

COMPUTER SUPPORTED COOPERATIVE WORK; INFORMATION ANALYSIS; INFORMATION DISSEMINATION; INFORMATION RETRIEVAL; SEMANTICS;

COLLABORATIVE FILTERS; CONTENT BASED FILTERING; INFORMATION FILTERING; RECOMMENDATION SYSTEM;

ELECTRONIC COMMERCE;

EID: 33747873320     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-006-8771-2     Document Type: Article

References (31)

1. Anick, P. G., Brennan, J. D., Flynn, R. A., Hanssen, D. R., Alvey, B., & Robbins, J. M. (1990). A direct manipulation interface for boolean information retrieval via natural language query. In Proc. of the ACM SIGIR-90 (pp. 135-150).
2. Baeza-Yates, R., & Riberio-Neto, B. (1999). Modern information retrieval (p. 30). Reading, MA: Addison-Wesley Publishing Co.
3. Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
4. Basu, C., & Cohen, W. W. (1998). Using social and content-based information in recommendation. In Proc. of the AAAI-98.
5. Bradley, P. S., & Fayyad, U. M. (1998) Refining initial points for K-means clustering. In Proc. of ICML '98 (pp. 91-99).
6. Breese, J. S., Heckerman, D., & Kardie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proc. Of UAI (pp. 43-52).
7. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. (1999). Combining content-based and collaborative filters in an online newspaper. In ACM SIGIR '99 Workshop on Recommender Systems.
8. Delgado, J., Ishii, N., & Ura, T. (1998). Content-based collaborative information filtering: actively learning to classify and recommend documents. In Proc. of the CIA '98 (pp. 206-215).
9. Duda, R. O., Hart, P. E., & Stork, D. G. (2000) Pattern classfication (pp. 528-530). New York: Wiley-Interscience Publication.
10. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM Science, 35, 61-70.
11. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). Combininig collaborative filtering with personal agents for better recommendations. In Proc. of the AAAI-99.
12. Gupta, D., Digiovanni, M., Narita, H., & Goldberg, K. (1999). Jester 2.0: A new linear-time collaborative filtering algorithm applied to jokes. In Proc. of ACM SIGIR '99 Workshop on Recommender Systems.
13. Hauver, D. B., & French, J. C. (2001). Flycasting: Using collaborative filtering to generate a play list for online radio. In Proc. of Web Delivery of Music.
14. Herlocker, J., Konstan, J., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proc. of SIGIR-99.
15. Kim, M., & Raghavan, V. V. (2000). Adaptive concept-based retrieval using a neural network. In Proc. Of ACM SIGIR 2000 Workshop on Mathematical/Formal Methods in Information Retrieval.
16. Lee, J. H., Kim, M. H., & Lee, Y. H. (1993). Ranking documents in thesaurus-based boolean retrieval systems. Information Processing and Management, 50(1), 79-91.
17. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proc. of the 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281-297).
18. Oard, D. W., & Marchionini, G. (1996). A conceptual framework for text filtering. Technical Report EE-TR-96-25, CAR-TR-830. University of Maryland.
19. Ogawa, Y., Morita, T., & Kobayashi, K. (1991). A fuzzy document retrieval system using the keyword connection matrix and a learning method. Fuzzy Sets and Systems, 39, 163-179.
20. Popescul, A., Ungar, L. H., Pennock, D. M., & Lawrence, S. (2001). Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proc. of UAI 2001.
21. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proc. of CCSCW (pp. 175-186).
22. Robertson, S. E., & Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129-146.
23. Salton, G., & Buckley, C. (1988). Term-weight approaches in automatic retrieval. Information Processing and Management, 24(5), 513-523.
24. Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.
25. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proc. of WWW (pp. 285-295).
26. Terry, D. B. (1993). A tour through tapestry. In Proc. of COOCS (pp. 21-30).
27. Upendra, S., & Patti, M. (1995). Social information filtering: Algorithms for automating "word of mouth". In Proc. of ACM CHI '95 (pp. 210-217).
28. Verhoeff, J., Goffman, W., & Belzer, J. (1961). Inefficiency of the use of the boolean functions for information retrieval systems. Communications of the ACM, 4, 557-558, 594.
29. Wasfi, A. M. A. (1999). Collecting user access patterns for building user profiles and collaborative filtering. In Proc. of IUI (pp. 57-64).
30. Wilkinson, R., & Hingston, P. (1991). Using the cosine measure in a neural network for document retrieval. In Proc. of ACM SIGIR (pp. 202-210).
31. Wittenburg, K., Das, D., Hill, W., & Stead, L. (1995). Group asynchronous browsing on the world wide web. In Proc. of the Fourth WWW (pp. 51-62).