Collaborative Similarity Embedding for Recommender Systems
This work addresses the challenge of improving recommendation accuracy in sparse data scenarios, which is a common issue in real-world applications, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of learning representations from sparse user-item graphs in recommender systems by proposing a unified framework that exploits both direct and higher-order proximity relations, resulting in significantly better performance than state-of-the-art methods on eight benchmark datasets.
We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation. In the proposed framework, we differentiate two types of proximity relations: direct proximity and k-th order neighborhood proximity. While learning from the former exploits direct user-item associations observable from the graph, learning from the latter makes use of implicit associations such as user-user similarities and item-item similarities, which can provide valuable information especially when the graph is sparse. Moreover, for improving scalability and flexibility, we propose a sampling technique that is specifically designed to capture the two types of proximity relations. Extensive experiments on eight benchmark datasets show that CSE yields significantly better performance than state-of-the-art recommendation methods.