ReST: A Plug-and-Play Spatially-Constrained Representation Enhancement Framework for Local-Life Recommendation
Hao Jiang, Long Zhang, Guoquan Wang, Sheng Yu, Yang Zeng, Wencong Zeng, Fei Pan, Peng Jiang, Guorui Zhou

TL;DR
This paper introduces ReST, a plug-and-play framework that enhances long-tail item representations for local-life recommendation by leveraging spatial constraints and contrastive learning.
Contribution
It proposes a novel item-centric approach with a spatially-constrained enhancement network and strategies for better long-tail item representation in local-life recommendation.
Findings
Improves long-tail item representation quality.
Enhances recommendation accuracy within spatial constraints.
Effectively captures latent item relationships.
Abstract
Local-life recommendation have witnessed rapid growth, providing users with convenient access to daily essentials. However, this domain faces two key challenges: (1) spatial constraints, driven by the requirements of the local-life scenario, where items are usually shown only to users within a limited geographic area, indirectly reducing their exposure probability; and (2) long-tail sparsity, where few popular items dominate user interactions, while many high-quality long-tail items are largely overlooked due to imbalanced interaction opportunities. Existing methods typically adopt a user-centric perspective, such as modeling spatial user preferences or enhancing long-tail representations with collaborative filtering signals. However, we argue that an item-centric perspective is more suitable for this domain, focusing on enhancing long-tail items representation that align with the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
