Disentangling ID and Modality Effects for Session-based Recommendation
arxiv(2024)
摘要
Session-based recommendation aims to predict intents of anonymous users based
on their limited behaviors. Modeling user behaviors involves two distinct
rationales: co-occurrence patterns reflected by item IDs, and fine-grained
preferences represented by item modalities (e.g., text and images). However,
existing methods typically entangle these causes, leading to their failure in
achieving accurate and explainable recommendations. To this end, we propose a
novel framework DIMO to disentangle the effects of ID and modality in the task.
At the item level, we introduce a co-occurrence representation schema to
explicitly incorporate cooccurrence patterns into ID representations.
Simultaneously, DIMO aligns different modalities into a unified semantic space
to represent them uniformly. At the session level, we present a multi-view
self-supervised disentanglement, including proxy mechanism and counterfactual
inference, to disentangle ID and modality effects without supervised signals.
Leveraging these disentangled causes, DIMO provides recommendations via causal
inference and further creates two templates for generating explanations.
Extensive experiments on multiple real-world datasets demonstrate the
consistent superiority of DIMO over existing methods. Further analysis also
confirms DIMO's effectiveness in generating explanations.
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