Transparent Text-Based Item Recommendation: Introducing TRecX

Abstract

Recommender systems have proven their usefulness both for companies and customers. The former increase their sales and the latter get a more satisfying shopping experience. These systems can benefit from the advent of explainable artificial intelligence since a well-explained recommendation will be more convincing and may broaden the customer’s purchasing options. Many approaches offer justifications for their recommendations based on the similarity (in some sense) between users, past purchases, etc., which requires some knowledge of the users. In this paper, we present a recommender system with explanatory capabilities which can deal with the so-called cold-start problem since it does not require any previous knowledge of the user. Our method learns the relationship between the products and some relevant words appearing in the textual reviews from previous customers for those products. Then, starting from the textual query of a user’s request for a recommendation, our approach elaborates a list of products and explains each recommendation based on the compatibility between the query’s words and the relevant terms for each product.