App Recommendation Model Based on Heterogeneous Graph Collaborative Filtering

With the explosion in the number of mobile applications (apps), it becomes increasingly difficult to find a suitable app that aligns with user’s needs and preferences. Recommender systems help simplify this process as they have done in the field of e-commerce. However traditional recommendation methods suffer from poor performance, if their designs are not well tailored to the specific characteristics of mobile application. In fact, when recommending apps, their categories play a sensitive role. Meanwhile, recently emerged deep learning-based methods come with high computational costs, limiting their applicability in the lightweight mobile scenarios. To address these issues, in this paper, we propose a heterogeneous graph collaborative filtering approach applied to app recommendations. Firstly, we propose a review processing method that can effectively capture the features of users, apps and categories through reviews and likes. Then, we build a heterogeneous graph containing multiple nodes such as users, apps and categories, and further learn the latent features and complex relationships of nodes through lightweight graph convolution operations. Finally, we predict the apps for users by node embeddings. The extensive experimental results based on real dataset demonstrate the proposed model has achieved significant improvement in recommendation performance compared with the baselines.