Wasserstein Collaborative Filtering for Item Cold-start Recommendation

Although numerous instantiations [ He et al., 2017; Liang et al., 2018 ] of CF have been proposed in recent years, matrix factorization (MF) [ Mnih and Salakhut-dinov, 2007; Koren et al., 2009 ] remains the most popular one due to its simplicity and effectiveness, and has been used for large scale recommendations of news [ Das et al., 2007], movies [ Koren et al., 2009 ] and products [ Linden et al., 2003 ] . Recent studies extend the MF framework for item cold-start recommendation by incorporating content information of items. The majority of methods for item cold-start recommendation employ a latent space sharing model. For example, Saveski te al. [ 2014] and Barjasteh et al. [ 2016 ] propose to use MF as the prjection function for both interactions and item contents. LDA [ Wang and Blei, 2011 ], CNN [ Kim et al., 2016 ], DNN [ Ebesu and Fang, 2017 ], SDAE [ Wang et al., 2015; Ying et al., 2016 ] and mDA [ Li et al., 2015 ] are proposed to learn the latent vectors of items from their textual contents. V an den Oord et al. [ 2013] and Wang et al. [ 2014] propose to use CNN to learn the latent vectors of music from their audio signals. The Wasserstein distance, which originates from optimal transport theory [ Rubner et al., 1998; Levina and Bickel, 2001], is a distance metric on probabilistic space and able to leverage the information on feature space. It has been successfully applied to many applications, such as computer vision [ Arjovsky et al., 2017 ] and natural language processing Figure 2: An illustration of problem definition.