TL;DR
This paper introduces DGCF, a novel graph-based collaborative filtering model that disentangles user intents to improve recommendation accuracy and interpretability by modeling diverse user-item relationship factors.
Contribution
The paper proposes a new disentangled graph collaborative filtering model that explicitly captures and separates different user intents in representations, enhancing recommendation quality.
Findings
DGCF outperforms state-of-the-art models on benchmark datasets.
Disentangled representations improve interpretability of user preferences.
Model effectively captures diverse user-item relationship factors.
Abstract
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering…
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Taxonomy
MethodsInterpretability
