To what extent does the expandable side-MoE architecture improve robustness to distribution shift in user-item

Abstract

Abstract: Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing users' latest preferences is challenging, as interactions reflecting recent interests are limited and new items often lack sufficient feedback. A common solution is to enrich item representations using multimodal encoders (e.g., BERT or ViT) to extract visual and textual features. However, these encoders are pretrained on general-purpose tasks: they are not tailored to user preference modeling, and they overlook the f

Research Question

To what extent does the expandable side-MoE architecture improve robustness to distribution shift in user-item interactions compared to standard continual learning methods (e.g., EWC, SI) on multimodal sequential recommendation datasets like Yelp and Steam?

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