Pairwise interaction tensor factorization for personalized tag recommendation

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RENDLE, Steffen, Lars SCHMIDT-THIEME, 2010. Pairwise interaction tensor factorization for personalized tag recommendation. the third ACM international conference. New York, New York, USA, 4. Feb 2010 - 6. Feb 2010. In: Proceedings of the third ACM international conference on Web search and data mining - WSDM '10. the third ACM international conference. New York, New York, USA, 4. Feb 2010 - 6. Feb 2010. New York, New York, USA:ACM Press, pp. 81. ISBN 978-1-60558-889-6

@inproceedings{Rendle2010Pairw-12685, title={Pairwise interaction tensor factorization for personalized tag recommendation}, year={2010}, doi={10.1145/1718487.1718498}, isbn={978-1-60558-889-6}, address={New York, New York, USA}, publisher={ACM Press}, booktitle={Proceedings of the third ACM international conference on Web search and data mining - WSDM '10}, author={Rendle, Steffen and Schmidt-Thieme, Lars} }

eng Rendle, Steffen 2011-09-08T06:07:58Z deposit-license Pairwise interaction tensor factorization for personalized tag recommendation Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. 2011-09-08T06:07:58Z Schmidt-Thieme, Lars First publ. in: WSDM : proceedings of the Third ACM International Conference on Web Search & Data Mining; February 3 - 6, 2010, New York City, NY, USA. New York: ACM, 2010, pp. 81-90 Schmidt-Thieme, Lars 2010 Rendle, Steffen

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