Double-Wing Mixture of Experts for Streaming Recommendations
Yan Zhao, Shoujin Wang, Yan Wang, Hongwei Liu, and Weizhe Zhang

TL;DR
This paper introduces VRS-DWMoE, a novel framework combining variational sampling and a double-wing mixture of experts to enhance streaming recommendation accuracy by capturing user preference drift and heterogeneity.
Contribution
It proposes a new sampling method and a double-wing MoE model to better learn and adapt to long-term and heterogeneous user preferences in streaming recommendations.
Findings
VRS-DWMoE outperforms state-of-the-art SRSs in accuracy.
The framework effectively captures long-term user preferences.
It handles data heterogeneity better than existing models.
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Bandit Algorithms Research · Data Stream Mining Techniques
