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.
Abstract (translated)
GPRec 以可学习的方式明确地将用户分类为不同的组，并将这些组与相应的组嵌入对齐。我们设计了双重组嵌入空间，通过对比正负模式来提供多元化的组偏好视角。在个体层面，GPRec 能够从类似ID的特征中识别个人偏好，并优化获得的个体表示，使其独立于组表示，从而为组级别建模提供强大的补充。此外，我们还提出了一些灵活地将 GPRec 整合到各种DRS模型中的策略。通过对三个公开数据集进行严格的测试，GPRec 显示出了显著提高推荐质量的效果。
URL
https://arxiv.org/abs/2410.20730
