Learning Vertex Representations for Bipartite Networks
This work tackles the problem of improving representation learning for bipartite networks, which is incremental as it builds on existing NRL methods but focuses on a specific network type.
The paper addresses the lack of dedicated network representation learning methods for bipartite networks, proposing a new approach to learn vertex embeddings that capture the distinct properties of relationships between two entity types, such as in e-commerce or search systems.
Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages.