GPRec: Bi-level User Modeling for Deep Recommenders

TL;DR

GPRec is introduced, a novel bi-level user modeling approach that substantially improves DRS and 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.

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.

Figures (4)

• Figure 1: Framework overview.
• Figure 2: Ablation study for GPRec on ML1M and KuaiRand with MLP.
• Figure 3: Parameter analysis on ML1M with MLP.
• Figure 4: Similarity visualization for GPRec.