Abstract
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.
Original language | English |
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Pages (from-to) | 221-237 |
Number of pages | 17 |
Journal | Information Sciences |
Volume | 369 |
DOIs | |
Publication status | Published - 10 Nov 2016 |
Externally published | Yes |
Keywords
- Collaborative filtering
- Dating recommendation
- Markov random field
- Movie recommendation
- Recommender systems
- Sparse graph learning