通过双层建模区分用户群体与个人偏好，提升推荐精度
GPRec: Bi-level User Modeling for Deep Recommenders
- 将用户分组并学习群体嵌入，对比正负偏好模式
- 分离个体特征与群体特征，增强个性化推荐能力
- 可灵活接入各类推荐模型，适用于多场景
GPRec以可学习方式显式将用户划分为群体，并将其与对应的群体嵌入对齐。设计双群体嵌入空间，通过对比正负模式提供多样化的群体偏好视角。在个体层面，从ID类特征中识别个人偏好，并使个体表示独立于群体表示，从而为群体建模提供稳健补充。同时提出多种灵活集成策略，适用于不同深度推荐系统（DRS）模型。在三个公开数据集上的严格测试表明，该方法显著提升了推荐质量。
原文摘要 · Abstract (English)
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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