ABSTRACT
Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
- M. Berry, S. Dumais, and G. O'Brien. Using linear algebra for intelligent information retrieval. SIAM Review, 37(4):573--595, 1994. Google ScholarDigital Library
- J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. Conf. on Uncertainty in Artificial Intelligence, pages 43--52, 1998. Google ScholarDigital Library
- G. Furnas, S. Deerwester, and S. e. a. Dumais. Information retrieval using a singular value decomposition model of latent semantic structure. In Proc. ACM SIGIR Conf., pages 465--480, 1988. Google ScholarDigital Library
- S. Golder and B. Huberman. The structure of collaborative tagging systems. In Technical Report, 2005.Google Scholar
- H. Halpin, V. Robu, and H. Shepherd. The dynamics and semantics of collaborative tagging. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 211--220, 2007. Google ScholarDigital Library
- J. Herlocker, J. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5(4):287--310, 2002. Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. on Information Systems, 22(1):5--53, 2004. Google ScholarDigital Library
- A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In The Semantic Web: Research and Applications, pages 411--426, 2006. Google ScholarDigital Library
- Z. Huang, H. Chen, and D. Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1):116--142, 2004. Google ScholarDigital Library
- R. Jaschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Knowledge Discovery in Databases: PKDD 2007, pages 506--514.Google Scholar
- G. Karypis. Evaluation of item-based top-n recommendation algorithms. In Proc. ACM CIKM Conf., pages 247--254, 2001. Google ScholarDigital Library
- J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- L. d. Lathauwer, B. d. Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM Journal of Matrix Analysis and Applications, 21(4):1253--1278, 2000. Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and W. T. The pagerank citation ranking - bringing order to the web. In Technical Report, 1998.Google Scholar
- J. Sun, D. Shen, H. Zeng, Q. Yang, Y. Lu, and Z. Chen. Cubesvd: a novel approach to personalized web search. In World Wide Web Conference, pages 382--390, 2005. Google ScholarDigital Library
- H. Wang and N. Ahuja. A tensor approximation approach to dimensionality reduction. International Journal of Computer Vision, pages 217--229, 2007. Google ScholarDigital Library
- Y. Xu, L. Zhang, and W. Liu. Cubic analysis of social bookmarking for personalized recommendation. In Frontiers of WWW Research and Development - APWeb 2006, pages 733--738, 2006. Google ScholarDigital Library
- Z. Xu, Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. Collaborative Web Tagging Workshop, 2006.Google Scholar
Index Terms
- Tag recommendations based on tensor dimensionality reduction
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