Representing items as word-embedding vectors and generating recommendations by measuring their linear independence

In order to generate effective results, it is essential for a recommender system to model the information about the user interests in a profile. Even though word embeddings (i.e., vector representations of textual descriptions) have proven to be effective in many contexts, a content-based recommendation approach that employs them is still less effective than collaborative strategies (e.g., SVD). In order to overcome this issue, we introduce a novel criterion to evaluate the word-embedding representation of the items a user rated. The proposed approach defines a vector space in which the similarity between an unevaluated item and those in a user profile is measured in terms of linear independence. Experiments show its effectiveness to perform a better ranking of the items, w.r.t. collaborative filtering, both when compared to a latent-factor-based approach (SVD) and to a classic neighborhood user-based system.