Many social networking platforms track user-item interaction and friend relationships between users. The goal of this work is to learn about tastes that are shared by friends. We devise a probabilistic model in which tastes explain both friend relationships and item interactions as latent factors. After estimation, shared tastes can be used to predict common preferences of befriended users, predict further item interactions, and give an overview about the network of users that share the same taste.
|Published in||NIPS Workshop on Applications for Topic Models: Text and Beyond|