面向个体与群体的灵活推荐系统

中文摘要 AI 辅助

群体推荐系统通常依赖于聚合个体偏好或将群体视为独立的元用户。然而，这些方法往往受限于静态聚合策略或群体历史中的数据稀疏问题。本文提出了一种新颖方法，基于图神经网络（GNN）架构学习每个用户偏好的双重表示，一方面捕捉其作为独立个体的行为，另一方面捕捉其作为集体成员的行为。通过对这些个体导向和群体导向的偏好进行差异分析，我们的系统能够确定每个用户在加入群体时的行为特征。最后，定义了特定的偏好聚合策略，以应对构成群体的用户行为特征。因此，该系统同样能够为个体和任意群体提供精准推荐，有效统一了推荐系统的两种传统范式。在模拟多样化群体设置和行为的合成数据上的实验证实，与最先进的方法相比，所提方法具有灵活性和相关性。

英文摘要

Group recommender systems typically rely on either aggregating individual preferences or treating groups as distinct meta-users. However, these methods often suffer from static aggregation strategies or data sparsity issues within group histories. This paper introduces a novel approach, that relies on a GNN-based architecture to learn a dual representation of each user's preferences, capturing their behavior as an independent individual from one side and as a member of a collective from the other side. By performing a differential analysis of these individual and group-oriented preferences, our system then determines the behavioral profile of each user when joining a group. Finally, specific preference aggregation strategies are defined to cope with the behavioral profiles of the users composing a group. Consequently, the system is equally capable of delivering precise recommendations to individuals and to arbitrary groups, effectively unifying the two traditional paradigms of recommendation. Experiments on synthetic data simulating diverse group settings and behaviors confirm the flexibility and relevance of the proposed approach compared to state-of-the-art methods.

发表机构

• IMT Atlantique（IMT大西洋学院）
• Lab-STICC, UMR CNRS 6285（Lab-STICC，法国国家科学研究中心联合研究单位6285）
• Intescia Group（Intescia集团）

机构由 AI 辅助整理，请以论文原文为准。