Collaborative filtering embeddings for memory-based recommender systems

Word embeddings techniques have attracted a lot of attention recently due to their effectiveness in different tasks. Inspired by the continuous bag-of-words model, we present prefs2vec, a novel embedding representation of users and items for memory-based recommender systems that rely solely on user-item preferences such as ratings. To improve the performance and prevent overfitting, we use a variant of dropout as regularization, which can leverage existent word2vec implementations. Additionally, we propose a procedure for incremental learning of embeddings that boosts the applicability of our proposal to production scenarios. The experiments show that prefs2vec with a standard memory-based recommender system outperforms all the state-of-the-art baselines in terms of ranking accuracy, diversity, and novelty.