Journal
NEUROCOMPUTING
Volume 386, Issue -, Pages 208-220Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2019.12.046
Keywords
Markov chains; User cold-start; Temporal information; Social network embedding
Categories
Funding
- National Natural Science Foundation of China [61976103, 61872161]
- Nature Science Foundation of Jilin Province [20180101330JC]
- Scientific and Technological Development Program of Jilin Province [20190302029GX]
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The primary objective of recommender systems is to help users select their desired items, where a key challenge is providing high-quality recommendations to users in a cold-start situation. Recent advances in tackling this problem combine social relations and temporal information and integrate them into a unified framework. However, these methods suffer from a limitation that there not always exist links for the newcomers, thus these users are filtered in related studies. To break the boundary, in this paper, we propose a Joint Personalized Markov Chains (JPMC) model to address the cold-start issues for implicit feedback recommendation system. In our study, we first utilize user embedding to mine Network Neighbors, so that newcomers without relations can be represented by similar users, then we designed a two-level model based on Markov chains at both user level and user group level respectively to model user preferences dynamically. Experimental results on three real-world datasets have shown that our model can significantly outperform the state-of-the-art models. (c) 2019 Elsevier B.V. All rights reserved.
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