Leveraging auxiliary textual data can help with user profiling and item characterization in recommender systems (RSs). However, incomplete user and item descriptions limit the potential of textual information. To this end, we propose a graph-aware convolutional large language model (LLM) method, eliciting LLMs to summa...
Ying-Peng Du, Ziteng Wang, Zhu Sun et al.· ACM Transactions on Informat...· 0 citations
Auction-based Federated Learning (AFL) provides a principled framework for incentivizing self-interested data owners (DOs) to participate in collaborative learning initiated by data consumers (DCs) through market mechanisms. A central challenge in AFL is to determine how a budget-constrained DC should bid for data-use...
Xiao-Li Tang, Ying-Peng Tang, Zhuang Qi et al.· IEEE Transactions on Pattern...· 0 citations
This paper departs from the stepwise MDP paradigm and reformulate AFL bidding as a Black-Box Optimization (BBO) problem, and proposes BBO-AFL (Budget-Efficient BBO for DC in AFL), a Budget-Aware Exploration mechanism that decouples high-frequency market cost signals from low-frequency model utility feedback.
Xiao-Li Tang, Haoran Shi, Han Yu 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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