CARES: a ranking-oriented CADAL recommender system

ABSTRACT

A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance.

REFERENCES

• 1: China-America Digital Academic Library. http://www.cadal.zju.edu.cn.
• 2: C. Wang. Research and implementation of digital library personalization recommendation system. A Dissertation Submitted to Zhejiang University for Degree of Master of Engineering, 2007.
• 3: J. S. Breese, D. Heckerman, and C. M. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, pages 43--52, 1998.
• 4: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 5: Jon Herlocker , Joseph A. Konstan , John Riedl, An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms, Information Retrieval, v.5 n.4, p.287-310, October 2002 [doi>10.1023/A:1020443909834]
• 6: Rong Jin , Luo Si , ChengXiang Zhai , Jamie Callan, Collaborative filtering with decoupled models for preferences and ratings, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA [doi>10.1145/956863.956922]
• 7: Gui-Rong Xue , Chenxi Lin , Qiang Yang , WenSi Xi , Hua-Jun Zeng , Yong Yu , Zheng Chen, Scalable collaborative filtering using cluster-based smoothing, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076056]
• 8: Hao Ma , Irwin King , Michael R. Lyu, Effective missing data prediction for collaborative filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277751]
• 9: Ken Goldberg , Theresa Roeder , Dhruv Gupta , Chris Perkins, Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, v.4 n.2, p.133-151, July 2001 [doi>10.1023/A:1011419012209]
• 10: Thomas Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.89-115, January 2004 [doi>10.1145/963770.963774]
• 11: David M. Pennock , Eric Horvitz , Steve Lawrence , C. Lee Giles, Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, p.473-480, June 30-July 03, 2000
• 12: Nathan N. Liu , Qiang Yang, EigenRank: a ranking-oriented approach to collaborative filtering, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390351]
• 13: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 14: M. G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2):81--93, 1938.
• 15: D. D. Wackerly, W. Mendenhall, and R. L. Scheaffer. Mathematical Statistics with Application. Duxbruy Thomson Learning, Pacific Grove, CA. USA, sixth edition, 2002.
• 16: Emine Yilmaz , Javed A. Aslam , Stephen Robertson, A new rank correlation coefficient for information retrieval, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390435]
• 17: W. W. Cohen, R. E. Schapire, and Y. Singer. Learning to order things. J. Artif. Intell. Res. (JAIR), 10:243--270, 1999.
• 18: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, v.30 n.1-7, p.107-117, April 1, 1998
• 19: Taher H. Haveliwala, Topic-sensitive PageRank, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511513]
• 20: M. Richardson and P. Domingos. The intelligent surfer: Probabilistic combination of link and content information in pagerank. In NIPS, pages 1441--1448, 2001.
• 21: Glen Jeh , Jennifer Widom, Scaling personalized web search, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775191]
• 22: Kalervo Järvelin , Jaana Kekäläinen, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems (TOIS), v.20 n.4, p.422-446, October 2002 [doi>10.1145/582415.582418]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries
          Subjects: Systems issues

General Terms:
Experimentation

Keywords:
collaborative filtering, digital library, recommendation system