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CARS2: Learning Context-aware Representations for Context-aware Recommendations

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 la-
tent 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.