Recommendation

Diversified recommendation with weighted hypergraph embedding: Case study in music
Recommender systems serve a dual purpose for users sifting out inappropriate or mismatched information while accurately identifying …
Chaoguang Luo, Liuying Wen, Yong Qin, Philip S. Yu, Liangwei Yang, Zhineng Hu
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Collaborative Alignment for Recommendation
Traditional recommender systems have primarily relied on identity representations (IDs) to model users and items. Recently, the …
Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu
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Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent …
Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zhou, Ke Xu, Liancheng Fang, Philip S. Yu
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Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation
Cross-market recommendation (CMR) involves selling the same set of items across multiple nations or regions within a transfer learning …
Chen Wang, Ziwei Fan, Liangwei Yang, Mingdai Yang, Xiaolong Liu, Zhiwei Liu, Philip S. Yu
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Instruction-based Hypergraph Pretraining
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to …
Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
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Knowledge Graph Context-Enhanced Diversified Recommendation
The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users’ historical …
Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu
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Unified Pretraining for Recommendation via Task Hypergraphsn
Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender …
Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
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