Knowledge Graph-based recommender systems naturally produce explainable recommendations, by showing the reasoning paths in the knowledge graph (KG) that were followed to select the recommended items. One can define metrics that assess the quality of the explanation paths in terms of recency, popularity, and diversity. Combining in- and post-processing approaches to optimize for both recommendation …
Category: Explainability
Hands on Explainable Recommender Systems with Knowledge Graphs
This tutorial was presented at the RecSys ’22 conference, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras. On the tutorial’s website, you can find the slides, the video recording of our talk, and the notebooks of the hands-on parts. The goal of this tutorial was to present the RecSys community with recent advances on explainable …
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Being able to assess explanation quality in recommender systems and by shaping recommendation lists that account for explanation quality allows us to produce more effective recommendations. These recommendations can also increase explanation quality according to the proposed properties, fairly across demographic groups. In a SIGIR 2022 paper, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras, …