GPRec: Bi-level User Modeling for Deep Recommenders
Journal:
arXiv
Published Date:
Oct 28, 2024
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.
