Sequential recommendation (SR) systems are widely deployed across modern online platforms, and have been shown to be vulnerable to poisoning attacks. Such attacks inject fabricated user sequences into training data to promote target items. Existing methods achieve stealthiness by enforcing surface-level similarity to g...
Hang-Chang Zhou, Hong-Xu Ma, Hui Fang et al.· Proceedings of the 20th ACM...· 0 citations
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
Feature selection is indispensable for mitigating overfitting and reducing feature redundancy in high-dimensional scenarios. However, most existing approaches rely on unstable overall separability and sample-wise geometry, thereby neglecting the stable class-specific discriminative structures and leading to poor perfor...
Mao Li, Zhilong Mi, Yingpeng Du et al.· Proceedings of the 32nd 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
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