A multi-behavior recommendation model,explainable local and global comparative multi-behavioral recommendation,was proposed to address the problems that traditional multi-behavior recommendation models cannot efficiently learn the complex structure of heterogeneous information networks and lack interpretability.A meta-path view,widely used to extract higher-order structures,was employed to capture the features between each node from a specific semantic perspective.A hyper meta-path graph capturing meta-path features and interaction information between meta-paths was designed to capture the interaction infor-mation between multiple meta-paths,distinguishing different behavioral patterns of various users facing items of different catego-ries.A heterogeneity-explainable contrastive learning was used to determine the importance of behavior types yielding higher quality positive and negative samples for comparison.The proposed model outperforms mainstream advanced recommendation models in experiments on two public datasets.
关键词
推荐模型/异构信息网络/多行为推荐/全局结构/元路径/可解释性/对比学习
Key words
recommendation model/heterogeneous information networks/multi-behavior recommendation/higher-order struc-tures/meta-path/interpretability/contrastive learning
维普
万方数据