Cars2

Rich contextual information is typically available in many recommendation domains allowing recommender systems to model the subtle effects of context on preferences. Most contextual models assume that the context shares the same latent space with the users and items. In this work we propose CARS2, a novel approach for learning context-aware representations for context-aware recommendations. We show that the context-aware representations can be learned using an appropriate model that aims to represent the type of interactions between context variables, users and items. We adapt the CARS2 algorithms to explicit feedback data by using a quadratic loss function for rating prediction, and to implicit feedback data by using a pairwise and a listwise ranking loss functions for top-N recommendations. By using stochastic gradient descent for parameter estimation we ensure scalability. Experimental evaluation shows that our CARS2 models achieve competitive recommendation performance, compared to several state-of-the-art approaches.