Accurate Mobile App Recommendation via Hypergraph Contrastive Collaborative Filtering

Mingdong Tang, Yinglin Huang, Fenfang Xie

Abstract: The exponential growth of mobile applications (apps) have made it increasingly challenging for users to discover apps that align with their interests. To address this challenge, researchers have drawn inspiration from the success of recommender systems in various domains, such as e-commerce, and developed app recommendation methods. However, existing approaches still face significant limitations, including over-smoothing, noise interference in high-dimensional data, semantic loss, and low-quality positive and negative samples, all of which hinder recommendation performance. To tackle the above limitations, this study proposes HCAppRec, a novel app recommendation approach that leverages user-app interaction history and integrates hypergraph neural networks with contrastive learning. HCAppRec first constructs a couple of hypergraphs by exploring the semantic similarities between users and between apps, derived from the user-app interaction data. Then, by integrating hypergraph neural networks with contrastive learning, HCAppRec can not only capture complex high-order relationships among users and apps but also distinguish subtle differences, enhancing the model's robustness and generalization. Extensive experiments on real-world datasets demonstrated that HCAppRec significantly outperforms state-of-the-art methods in comprehensive recommendation performance.

External IDs:dblp:conf/icws/TangHX25