Steffen Rendle
Lars Schmidt-Thieme
ABSTRACT
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.
Supplemental Material
- A. Herschtal and B. Raskutti. Optimising area under the roc curve using gradient descent. In ICML '04: Proceedings of the twenty-first international conference on Machine learning. ACM, 2004. Google Scholar
Digital Library
- P. Heymann, D. Ramage, and H. Garcia-Molina. Social tag prediction. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 531--538. ACM, 2008. Google ScholarDigital Library
- A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information Retrieval in Folksonomies: Search and Ranking. 2006.Google Scholar
- R. Jaschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland, 2007.Google ScholarDigital Library
- R. Jaschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in social bookmarking systems. AI Communications, pages 231--247, 2008. Google ScholarDigital Library
- T. G. Kolda and J. Sun. Scalable tensor decompositions for multi-aspect data mining. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008. Google ScholarDigital Library
- L. D. Lathauwer, B. D. Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl., 21(4):1253--1278, 2000. Google ScholarDigital Library
- L. D. Lathauwer, B. D. Moor, and J. Vandewalle. On the best rank-1 and rank-(r1,r2,. . .,rn) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl., 21(4):1324--1342, 2000. Google ScholarDigital Library
- S. Rendle and L. Schmidt-Thieme. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems. ACM, 2008. Google ScholarDigital Library
- J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML '05: Proceedings of the 22nd international conference on Machine learning. ACM, 2005. Google ScholarDigital Library
- A. Shashua and T. Hazan. Non-negative tensor factorization with applications to statistics and computer vision. In ICML '05: Proceedings of the 22nd international conference on Machine learning, pages 792--799. ACM, 2005. Google ScholarDigital Library
- Y. Song, L. Zhang, and C. L. Giles. A sparse gaussian processes classification framework for fast tag suggestions. In CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 93--102. ACM, 2008. Google ScholarDigital Library
- Y. Song, Z. Zhuang, H. Li, Q. Zhao, J. Li, W.-C. Lee, and C. L. Giles. Real-time automatic tag recommendation. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 515--522. ACM, 2008. Google ScholarDigital Library
- P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos. Tag recommendations based on tensor dimensionality reduction. In RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems, pages 43--50. ACM, 2008. Google ScholarDigital Library
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- Learning optimal ranking with tensor factorization for tag recommendation
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