基于异构信息网络的紧耦合推荐算法

Tightly Coupled Recommendation Algorithm Based on Heterogeneous Information Networks

针对目前协同过滤方法面临的稀疏性问题以及辅助信息的异构特性未被充分利用的问题,鉴于异构信息网络(HIN)在建模复杂异构信息方面的优势,文中提出了一种基于HIN的紧耦合推荐模型(HTCRec),利用异构信息网络嵌入和紧耦合协同过滤框架进行个性化推荐。该模型首先聚合HIN中的元路径及其路径实例,再使用注意力机制将目标用户或项目的辅助信息用各自聚合元路径的嵌入进行表示,然后显式地将元路径合并到紧耦合交互模型中完成个性化推荐。在真实数据集上的实验结果表明,HTCRec模型较其他常用推荐模型具有更好的推荐性能,有效地缓解了数据稀疏问题。

In view of the problems of sparsity and underutilization of the heterogeneity of auxiliary information faced by current collaborative filtering methods and the advantages of heterogeneous information networks(HIN)in mo-deling complex heterogeneous information,a HIN based tightly coupled recommendation model(HTCRec)was proposed in this paper.It utilizes the heterogeneous information network embedding and a tightly coupled collaborative filtering framework to carry out personalized recommendation.Firstly,it aggregates meta-paths in a HIN and their corresponding path instances.Then it uses the attention mechanism to represent the auxiliary information of the target users or items in terms of the embedding of the respective aggregation meta-paths.At last,the meta-path is explicitly incorporated into the tightly coupled interaction model for personalized recommendation.The experimental results of the real data sets show that compared with the state-of-the-art recommendation models,the HTCRec model has better recommendation performance and effectively alleviates the problem of data sparsity.