HeteroMF: recommendation in heterogeneous information networks using context dependent factor models

WWW, pp. 643-654, 2013.

Cited by: 70|Bibtex|Views86|DOI:https://doi.org/10.1145/2488388.2488445
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in th...More

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