document: Learning optimal ranking with tensor factorization for tag recommendation 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 .
doc_bio_tags: O O B O B I O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O