GPRec: Bi-level User Modeling for Deep Recommenders

• URL: http://arxiv.org/abs/2410.20730v1
• Date: Mon, 28 Oct 2024 04:49:05 GMT
• Title: GPRec: Bi-level User Modeling for Deep Recommenders
• Authors: Yejing Wang, Dong Xu, Xiangyu Zhao, Zhiren Mao, Peng Xiang, Ling Yan, Yao Hu, Zijian Zhang, Xuetao Wei, Qidong Liu,
• Abstract summary: GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. On the individual level, GPRec identifies personal preferences from ID-like features and refines the obtained individual representations to be independent of group ones. Rigorous testing of GPRec on three public datasets has demonstrated significant improvements in recommendation quality.
• Score: 45.38687843911628
• License:
• Abstract: GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a diverse perspective on group preferences by contrasting positive and negative patterns. On the individual level, GPRec identifies personal preferences from ID-like features and refines the obtained individual representations to be independent of group ones, thereby providing a robust complement to the group-level modeling. We also present various strategies for the flexible integration of GPRec into various DRS models. Rigorous testing of GPRec on three public datasets has demonstrated significant improvements in recommendation quality.

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