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Diffusion Model for Interest Refinement in Multi-Interest Recommendation
Yankun Le, Haoran Li, Baoyuan Ou +4
Multi-interest candidate matching plays a pivotal role in personalized recommender systems, as it captures diverse user interests from their historical behaviors. Most existing met…
MOTOR: Learning ID-free Item Representation with Token Crossing for Embedding-based Multimodal Recommendation
Kangning Zhang, Jiarui Jin, Yingjie Qin +4
While multimodal recommendation models have effectively integrated visual and textual information, their reliance on unique ID embeddings constitutes a fundamental performance bott…
DREAM: A Dual Representation Learning Model for Multimodal Recommendation
Kangning Zhang, Yingjie Qin, Jiarui Jin +4
Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer f…
AlignRec: Aligning and Training in Multimodal Recommendations
Yifan Liu, Kangning Zhang, Xiangyuan Ren +7
With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainl…