Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2020

Abstract

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.

Keywords

recommendation, collaborative filtering, memory networks, high-order co-occurrences

Discipline

Computer Engineering | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the SIAM International Conference on Data Mining, Cincinnati, Ohio, U.S., 2020 May 7-9

First Page

1

Last Page

9

Identifier

10.1137/1.9781611976236.11

City or Country

Cincinnati, Ohio, U.S.

Additional URL

https://doi.org/10.1137/1.9781611976236.11

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