Multiplex memory network for collaborative filtering

• Main Authors: JIANG, Xunqiang, HU, Binbin, FANG, Yuan, SHI, Chuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: recommendation; collaborative filtering; memory networks; high-order co-occurrences; Computer Engineering; Data Storage Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5126; https://ink.library.smu.edu.sg/context/sis_research/article/6129/viewcontent/79.pdf
• Institution: Singapore Management University

Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services. Although current deep neural network-based collaborative filtering methods have achieved state-of-the-art performance in recommender systems, they still face a few major weaknesses. Most importantly, such deep methods usually focus on the direct interaction between users and items only, without explicitly modeling high-order co-occurrence contexts. Furthermore, they treat the observed data uniformly, without fine-grained differentiation of importance or relevance in the user-item interactions and high-order co-occurrence contexts. Inspired by recent progress in memory networks, we propose a novel multiplex memory network for collaborative filtering (MMCF). More specifically, MMCF leverages a multiplex memory layer consisting of an interaction memory and two co-occurrence context memories simultaneously, in order to jointly capture and locate important and relevant information in both user-item interactions and co-occurrence contexts. Lastly, we conduct extensive experiments on four datasets, and the results show the superior performance of our model in comparison with a suite of state-of-the-art methods.