RecSys

Llminit: A free lunch from large language models for selective initialization of recommendation
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across …
Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S Yu
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Sgcl: Unifying self-supervised and supervised learning for graph recommendation
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of …
Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu
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Training Large Recommendation Models via Graph-Language Tokens Alignment
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on …
Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
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Graph-sequential alignment and uniformity: Toward enhanced recommendation systems
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to …
Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu
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Taxonomy-guided zero-shot recommendations with llms
With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender …
Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu
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Personalized Multi-task Training for Recommender System
In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of …
Liangwei Yang, Zhiwei Liu, Jianguo Zhang, Rithesh Murthy, Shelbu Heinecke, Huan Wang, Caiming Xiong, Philip S. Yu
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