2009 LearningOptimalRankingwithTenso

• (Rendle et al., 2009b) ⇒ Steffen Rendle, Leandro Balby Marinho, Alexandros Nanopoulos, and Lars Schmidt-Thieme. (2009). “Learning Optimal Ranking with Tensor Factorization for Tag Recommendation.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557100

Subject Headings: Tensor Factorization.

Notes

• Categories and Subject Descriptors: I.2.6 Artificial Intelligence: Learning — Parameter learning.
• General Terms: Algorithms, Experimentation, Measurement, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Learning+optimal+ranking+with+tensor+factorization+for+tag+recommendation%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557100&preflayout=flat#citedby

Quotes

Author Keywords

Tensor Factorization, Ranking, Tag Recommendation

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.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 LearningOptimalRankingwithTenso	Steffen Rendle Lars Schmidt-Thieme Leandro Balby Marinho Alexandros Nanopoulos			Learning Optimal Ranking with Tensor Factorization for Tag Recommendation		KDD-2009 Proceedings		10.1145/1557019.1557100		2009