Semantics-aware Content Representations for Reproducible Recommender Systems (SCoRe)

Abstract

In the traditional categorization of recommendation techniques, content-based methods are often considered as an alternative to the most widely adopted collaborative filtering approaches. Content- based recommender systems suggest items similar to a user profile by matching attributes obtained by processing textual content. In order to deal with natural language ambiguity, semantics-aware rep- resentations can help to build more precise representations of users and items, and, in turn, to generate better recommendations. This tutorial (i) presents the most recent trends in the area of semantics- aware content-based recommender systems, including novel repre- sentation methods based on knowledge graphs and embedding techniques, (ii) discusses how to implement reproducible pipelines for semantics-aware recommender systems, and (iii) presents a new and comprehensive Python framework called ClayRS to deal with semantics-aware recommender systems.