Similarity (geometry)

Statistics

Machine learning

Exploit

Feature learning

Database

Artificial intelligence

Embedding

Scalability

PageRank

Recommender system

Graph

Collaborative filtering

Mathematics

Image (mathematics)

Pairwise comparison

Bipartite graph

Similarity learning

Data mining

Computer science

Theoretical computer science

Information retrieval

Flexibility (engineering)

Computer security

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.

Collaborative Similarity Embedding for Recommender Systems