Inspired by prior models (Koren et al., 2009;McAuley and Leskovec, 2013), we propose the following latent factor model to describe p u,c : γ U u and γ C c are K-dimensional latent vectors that encode topic-specific information (where K is the number of latent topics) for users and conversations.
γ U u reflects the topical interests of u, with higher value γ U u,k indicating greater interest by u in topic k. γ C c captures the extents that topics are discussed in conversation c. Similarly, D-dimensional vectors δ U u and δ C c capture discourse structures in shaping reply behaviors (where D is the number of discourse clusters).