整合用户多种行为数据，提升推荐系统精准度
Multi-Behavior Recommender Systems: A Survey
- 按点击、收藏、购买等行为建模输入数据
- 通过嵌入学习融合多行为特征，提升预测效果
- 适合研究推荐系统与电商平台优化的读者
传统推荐系统主要依赖单一类型用户-物品交互（如购买或评分）来预测偏好。但在实际场景中，用户会进行多种行为（如点击、加购），这些行为蕴含更丰富的兴趣信息。多行为推荐系统利用这些多样化交互来提升推荐质量，近年来研究迅速发展。本综述系统梳理多行为推荐系统的核心三步：(1) 数据建模：在输入层表示多行为；(2) 编码：将输入转换为向量表示（即嵌入）；(3) 训练：优化机器学习模型。我们基于各步骤方法的共性与差异，对现有系统进行分类，并讨论未来有前景的发展方向。
原文摘要 · Abstract (English)
Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems.
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