Exploiting a determinant-based metric to evaluate a word-embeddings matrix of Items

In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated items. Even though semantic technologies are rapidly evolving and word embeddings (i.e., vector representations of the words in a corpus) are effective in numerous information filtering tasks, at the moment collaborative approaches (such as SVD) still generate more accurate recommendations. However, this might happen because, by employing classic profiles in form of vectors that collect all the preferences of a user, the power of word embeddings at modeling texts could be affected. In this paper we represent a profile as a matrix of word-embedding vectors of the items a user evaluated, and present a novel determinant-based metric that measures the similarity between an unevaluated item and those in the matrix-based user profile, in order to generate effective content-based recommendations. Experiments performed on three datasets show the capability of our approach 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.