Collaborative filtering via sparse Markov random fields
Tran, Truyen, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Collaborative filtering via sparse Markov random fields, Information sciences, vol. 369, pp. 221-237, doi: 10.1016/j.ins.2016.06.027.
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Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method.
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