Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.

REFERENCES

• 1: Michael J. Pazzani, A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, v.13 n.5-6, p.393-408, Dec. 1999 [doi>10.1023/A:1006544522159]
• 2: Zan Huang , Hsinchun Chen , Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.116-142, January 2004 [doi>10.1145/963770.963775]
• 3: Zhou, T., Ren, J., Medo, M. and Zhang, Y.C. 2007 Bipartite network projection and personal recommendation. Phys Rev E 76.
• 4: Hyung Jun Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Information Sciences: an International Journal, v.178 n.1, p.37-51, January, 2008 [doi>10.1016/j.ins.2007.07.024]
• 5: 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]
• 6: Gori, M. and Pucci, A. 2007 ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. International Joint Conference on Artificial Intelligence.
• 7: Zan Huang , Daniel Zeng , Hsinchun Chen, A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce, IEEE Intelligent Systems, v.22 n.5, p.68-78, September 2007 [doi>10.1109/MIS.2007.80]
• 8: David Liben-Nowell , Jon Kleinberg, The link-prediction problem for social networks, Journal of the American Society for Information Science and Technology, v.58 n.7, p.1019-1031, May 2007 [doi>10.1002/asi.v58:7]
• 9: Kubica, J., Goldenberg, A., Komarek, P., Moore, A. and Schneider, J. 2003 A comparison of statistical and machine learning algorithms on the task of link completion. KDD Workshop on Link Analysis for Detecting Complex Behavior, 8.
• 10: Francois Fouss , Alain Pirotte , Jean-Michel Renders , Marco Saerens, Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation, IEEE Transactions on Knowledge and Data Engineering, v.19 n.3, p.355-369, March 2007 [doi>10.1109/TKDE.2007.46]
• 11: Huang, Z., Zeng, D. and Chen, H. 2004 A Unified Recommendation Framework Based on Probabilistic Relational Models. 4th Annual Workshop on Information Technologies and Systems.
• 12: Getoor, L. and Sahami, M. 1999 Using Probabilistic Relational Models for Collaborative Filtering. WebKDD.
• 13: Hasan, M.A., Chaoji, V., Salem, S. and Zaki, M. 2006 Link Prediction Using Supervised Learning. Workshop on Link Analysis, Counter-terrorism and Security.
• 14: Karsten M. Borgwardt , Cheng Soon Ong , Stefan Schönauer , S. V. N. Vishwanathan , Alex J. Smola , Hans-Peter Kriegel, Protein function prediction via graph kernels, Bioinformatics, v.21 n.1, p.47-56, January 2005 [doi>10.1093/bioinformatics/bti1007]
• 15: Xin Li , Hsinchun Chen , Zhu Zhang , Jiexun Li, Automatic patent classification using citation network information: an experimental study in nanotechnology, Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, June 18-23, 2007, Vancouver, BC, Canada [doi>10.1145/1255175.1255262]
• 16: Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C. 1999 Estimating the support of a high--dimensional distribution. MSR-TR-99-87, Microsoft Research.
• 17: Ting-Fan Wu , Chih-Jen Lin , Ruby C. Weng, Probability Estimates for Multi-class Classification by Pairwise Coupling, The Journal of Machine Learning Research, 5, p.975-1005, 12/1/2004

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human information processing

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

General Terms:
Algorithms, Design, Experimentation

Keywords:
collaborative filtering, kernel methods, recommender system