Taking advantage of images and texts in recommender systems: semantics and explainability

Traditionally, recommender systems are built on the users’ item consumption history. Many times, the users also make items reviews, giving us additional information in the form of text and images that are generally, not fully exploited. In this research we propose different approaches in the context of restaurants recommendation, where we take advantage of this information, by extracting a semantic meaning, in order to improve the traditional RS in terms of personalization and explainability.