Personalized ranking for digital libraries based on log analysis

ABSTRACT

Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVM^light as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59% accuracy in comparison to 50.02% for ranking SVM^light and below 43% for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.

REFERENCES

• 1: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 2: M. S. Aktas, M. A. Nacar, and F. Menczer. Personalizing pagerank based on domain profiles. In Proc. of WebKDD, 2004.
• 3: Brian Amento , Loren Terveen , Will Hill, Does “authority” mean quality? predicting expert quality ratings of Web documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.296-303, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345603]
• 4: K. Bharat and G. A. Mihaila. Hilltop: A search engine based on expert documents. In Proc. of the 9th International WWW Conference (Poster), 2000.
• 5: W. B. Croft, S. Cronen-Townsend, and V. Larvrenko. Relevance feedback and personalization: A language modeling perspective. In DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, 2001.
• 6: Abhinandan S. Das , Mayur Datar , Ashutosh Garg , Shyam Rajaram, Google news personalization: scalable online collaborative filtering, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242610]
• 7: Zhicheng Dou , Ruihua Song , Ji-Rong Wen, A large-scale evaluation and analysis of personalized search strategies, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242651]
• 8: Magdalini Eirinaki , Michalis Vazirgiannis, Web mining for web personalization, ACM Transactions on Internet Technology (TOIT), v.3 n.1, p.1-27, February 2003 [doi>10.1145/643477.643478]
• 9: D. Fogaras and B. Racz. Towards scaling fully personalized pagerank. In Proc. of Third Workshop on Algorithms and Models for the Web, 2004.
• 10: E. Garfield. Is citation analysis a legitimate evaluation tool? Scientometrics, 1:359--375, 1979.
• 11: M. Gker and C. Thompson. Personalized conversational case-based recommendation, 2000.
• 12: 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]
• 13: J. E. Hirsch. An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102:16569--16572, 2005.
• 14: 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]
• 15: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 16: Thorsten Joachims , Laura Granka , Bing Pan , Helene Hembrooke , Geri Gay, Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076063]
• 17: Seikyung Jung , Kevin Harris , Janet Webster , Jonathan L. Herlocker, SERF: integrating human recommendations with search, Proceedings of the thirteenth ACM international conference on Information and knowledge management, November 08-13, 2004, Washington, D.C., USA [doi>10.1145/1031171.1031277]
• 18: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), v.46 n.5, p.604-632, Sept. 1999 [doi>10.1145/324133.324140]
• 19: R. Krishnapuram, A. Joshi, O. Nasraoui, and L. Yi. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE-FS, 9:595--607, Aug. 2001.
• 20: A. Leubner and W. Kiebling. Personalized nonlinear ranking using full-text preferences. In Proc. SIGIR Workshop on Customised Information Delivery, 1999.
• 21: B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Knowledge Discovery and Data Mining, pages 80--86, 1998.
• 22: Eren Manavoglu , Dmitry Pavlov , C. Lee Giles, Probabilistic User Behavior Models, Proceedings of the Third IEEE International Conference on Data Mining, p.203, November 19-22, 2003
• 23: Bamshad Mobasher , Robert Cooley , Jaideep Srivastava, Automatic personalization based on Web usage mining, Communications of the ACM, v.43 n.8, p.142-151, Aug. 2000 [doi>10.1145/345124.345169]
• 24: Maurice D. Mulvenna , Sarabjot S. Anand , Alex G. Büchner, Personalization on the Net using Web mining: introduction, Communications of the ACM, v.43 n.8, p.122-125, Aug. 2000 [doi>10.1145/345124.345165]
• 25: A. Packard, M. K. H. Fan, and J. Doyle. A power method for the structured singular value. In Proceedings of the 27th IEEE Conference on Decision and Control, pages 2132--2137, 1988.
• 26: L. Page and S. Brin. The pagerank citation ranking: bring-ing order to the web. In Tech. report SIDL-WP-1999-0120, Stanford University, 1999.
• 27: Dmitry Pavlov , Eren Manavoglu , David M. Pennock , C. Lee Giles, Collaborative Filtering with Maximum Entropy, IEEE Intelligent Systems, v.19 n.6, p.40-48, November 2004 [doi>10.1109/MIS.2004.59]
• 28: Peter Pirolli , Stuart Card, Information foraging in information access environments, Proceedings of the SIGCHI conference on Human factors in computing systems, p.51-58, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223911]
• 29: 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]
• 30: Matthew Richardson , Amit Prakash , Eric Brill, Beyond PageRank: machine learning for static ranking, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135881]
• 31: Myra Spiliopoulou, Web usage mining for Web site evaluation, Communications of the ACM, v.43 n.8, p.127-134, Aug. 2000 [doi>10.1145/345124.345167]
• 32: Jaideep Srivastava , Robert Cooley , Mukund Deshpande , Pang-Ning Tan, Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2, January 2000 [doi>10.1145/846183.846188]
• 33: Y. Sun and C. L. Giles. Popularity weighted ranking for academic digital libraries. In 29th ECIR, pages 605--612. Springer-Verlag, 2007.
• 34: J. Teevan, S. Dumais, and E. Horvitz. Personalizing search via automated analysis of interests and activities, 2005.
• 35: Jun Xu , Hang Li, AdaRank: a boosting algorithm for information retrieval, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277809]

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.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
personalized ranking, web usage mining