Double-wing mixture of experts for streaming recommendations

Yan Zhao, Shoujin Wang, Yan Wang, Hongwei Liu*, Weizhe Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Streaming Recommender Systems (SRSs) commonly train recommendation models on newly received data only to address user preference drift, i.e., the changing user preferences towards items. However, this practice overlooks the long-term user preferences embedded in historical data. More importantly, the common heterogeneity in data stream greatly reduces the accuracy of streaming recommendations. The reason is that different preferences (or characteristics) of different types of users (or items) cannot be well learned by a unified model. To address these two issues, we propose a Variational and Reservoir-enhanced Sampling based Double-Wing Mixture of Experts framework, called VRS-DWMoE, to improve the accuracy of streaming recommendations. In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences. After that, we propose a Double-Wing Mixture of Experts (DWMoE) model to first effectively learn heterogeneous user preferences and item characteristics, and then make recommendations based on them. Specifically, DWMoE contains two Mixture of Experts (MoE, an effective ensemble learning model) to learn user preferences and item characteristics, respectively. Moreover, the multiple experts in each MoE learn the preferences (or characteristics) of different types of users (or items) where each expert specializes in one underlying type. Extensive experiments demonstrate that VRS-DWMoE consistently outperforms the state-of-the-art SRSs.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2020
Subtitle of host publication21st International Conference Amsterdam, The Netherlands, October 20–24, 2020 Proceedings, Part II
EditorsZhisheng Huang, Wouter Beek, Hua Wang, Rui Zhou, Yanchun Zhang
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages269-284
Number of pages16
ISBN (Electronic)9783030620080
ISBN (Print)9783030620073
DOIs
Publication statusPublished - 2020
Event21st International Conference on Web Information Systems Engineering, WISE 2020 - Amsterdam, Netherlands
Duration: 20 Oct 202024 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12343 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Web Information Systems Engineering, WISE 2020
CountryNetherlands
CityAmsterdam
Period20/10/2024/10/20

Keywords

  • Mixture of experts
  • Recommender system
  • Streaming recommendation

Fingerprint Dive into the research topics of 'Double-wing mixture of experts for streaming recommendations'. Together they form a unique fingerprint.

Cite this