NDLI: Probabilistic polyadic factorization and its application to personalized recommendation

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Probabilistic polyadic factorization and its application to personalized recommendation

Content Provider	ACM Digital Library
Author	Gong, Yihong Zhang, Yi Zhu, Shenghuo Chi, Yun
Abstract	Multiple-dimensional, i.e., polyadic, data exist in many applications, such as personalized recommendation and multiple-dimensional data summarization. Analyzing all the dimensions of polyadic data in a principled way is a challenging research problem. Most existing methods separately analyze the marginal relationships among pairwise dimensions and then combine the results afterwards. Motivated by the fact that various dimensions of polyadic data jointly affect each other, we propose a probabilistic polyadic factorization approach to directly model all the dimensions simultaneously in a unified framework. We then show the connection between the probabilistic polyadic factorization and a non-negative version of the Tucker tensor factorization. We provide detailed theoretical analysis of the new modeling framework, discuss implementation techniques for our models, and propose several extensions to the basic framework. We then apply the proposed models to the application of personalized recommendation. Extensive experiments on a social bookmarking dataset, Delicious, and a paper citation dataset, CiteSeer, demonstrate the effectiveness of the proposed models.
Starting Page	941
Ending Page	950
Page Count	10
File Format	PDF
ISBN	9781595939913
DOI	10.1145/1458082.1458206
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Probabilistic polyadic factorization Non-negative tensor factorization Personalized recommendation Multiple-dimensional data Social bookmarking
Content Type	Text
Resource Type	Article

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