GPRec: Bi-level User Modeling for Deep Recommenders
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 28 Oct 2024.
Builds on
32 of 46 references.
References · 32
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20 more
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.