ReST enhances long-tail item representations for spatially constrained local-life recommendations via a Meta ID Warm-up Network and a contrastive SIDENet with hard sampling and dynamic alignment strategies.
Fim: Frequency- aware multi-view interest modeling for local-life service recommendation,
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ReST: A Plug-and-Play Spatially-Constrained Representation Enhancement Framework for Local-Life Recommendation
ReST enhances long-tail item representations for spatially constrained local-life recommendations via a Meta ID Warm-up Network and a contrastive SIDENet with hard sampling and dynamic alignment strategies.