Collective embedding for neural context-aware recommender systems

Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, time became an important feature because user preferences tend to change over time or be similar in the near future. Researchers have proposed diferent models to incorporate time into their recommender system, however, the current models are not able to capture specifc temporal patterns. To address the limitation observed in previous works, we propose Collective embedding for Neural Context-Aware Recommender Systems (CoNCARS). The proposed solution jointly model the item, user and time embeddings to capture temporal patterns. Then, CoNCARS use the outer product to model the user-item-time correlations between dimensions of the embedding space. The hidden features feed our Convolutional Neural Networks (CNNs) to learn the non-linearities between the diferent features. Finally, we combine the output from our CNNs in the fusion layer and then predict the user's preference score. We conduct extensive experiments on real-world datasets, demonstrating CoNCARS improves the top-N item recommendation task and outperform the state-of-the-art recommendation methods.