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Recent years have witnessed a widespread increase of interest in networkrepresentation learning (NRL). By far most research efforts have focused on NRLfor homogeneous networks like social networks where vertices are of the sametype, or heterogeneous networks like knowledge graphs where vertices (and/oredges) are of different types. There has been relatively little researchdedicated to NRL for bipartite networks. Arguably, generic network embeddingmethods like node2vec and LINE can also be applied to learn vertex embeddingsfor bipartite networks by ignoring the vertex type information. However, thesemethods are suboptimal in doing so, since real-world bipartite networks concernthe relationship between two types of entities, which usually exhibit differentproperties and patterns from other types of network data. For example,E-Commerce recommender systems need to capture the collaborative filteringpatterns between customers and products, and search engines need to considerthe matching signals between queries and webpages.

Learning Vertex Representations for Bipartite Networks