Recommender systems model users and rank candidates within individual provider boundaries, fragmenting user context across services. User agents offer a different interaction model: they can act on the user’s behalf and seek recommendations across providers, but only if user context can travel with them. We present Tas...
Rong-Jie Zhu, Tian-Jun Wei, Cong Zhang et al.· Proceedings of the 20th ACM...· 0 citations
Large Language Models (LLMs) have shown strong potential for sequential reasoning, creating new opportunities for next Point-of-Interest (POI) recommendation. However, applying LLMs to POI prediction remains challenging due to the modality gap between textual semantics and continuous spatio-temporal signals. Existing r...
Nan Jiang, Haitao Yuan, Tian-Jun Wei et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes PLAN (Parallel Liquid-inspired Approximation Network), a lightweight representation learning framework that reformulates continuous liquid-state dynamics into a discretized and parallelizable formulation and acts as a versatile, plug-and-play backbone that generalizes to complex FJSP variants.
D. D. Kannan, Wei Zhang, Jieyi Bi et al.· 0 citations
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