WSHE: User feedback-based weighted signed heterogeneous information network embedding

Abstract

Heterogeneous information networks (HINs), which have rich semantic relations, can flexibly model multisource heterogeneous data in recommendation systems. Learning more comprehensive features of users based on HINs is a way to improve recommendation performance. User feedback can truly reflect user preferences. Most meta-path-based HIN embedding methods measure the similarity among users by counting the number of meta-paths and cannot fully learn the polar similarity of user preferences. In this work, we proposed a user feedback-based weighted signed HIN embedding method to learn more comprehensive embeddings of users and items. First, we defined a similarity measure using the weighted meta-path to measure the polar similarities of users. Second, we designed a weighted signed network embedding method based on the weighted sampling random walk. The embeddings of different meta-paths were deeply fused guided by an attention mechanism. The fused embeddings were further fused with attribute information using a pooling operation to capture their interactions. Finally, we utilized the rating prediction task to optimize the model and obtain the final embeddings of users and items. Extensive experiments performed on four datasets demonstrated the effectiveness of the model. In addition, we analyzed the importance of the different semantic meta-paths in the rating prediction task based on the interpretability of the attention mechanism.