Marcelo Garcia Manzato

arXiv2026

An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care

There are challenges that must be overcome to make recommender systems useful in healthcare settings. The reasons are varied: the lack of publicly available clinical data, the difficulty that users may have in understanding the reasons why a recommendation was made, the risks that may be involved in following that recommendation, and the uncertainty about its effectiveness. In this work, we address these challenges with a recommendation model that leverages the structure of psychometric data to provide visual explanations that are faithful to the model and interpretable by care professionals. We focus on a narrow healthcare niche, gerontological primary care, to show that the proposed recommendation model can assist the attending professional in the creation of personalised care plans. We report results of a comparative offline performance evaluation of the proposed model on healthcare datasets that were collected by research partners in Brazil, as well as the results of a user study that evaluates the interpretability of the visual explanations the model generates. The results suggest that the proposed model can advance the application of recommender systems in this healthcare niche, which is expected to grow in demand , opportunities, and information technology needs as demographic changes become more pronounced.

cs.AI↗

arXiv2025

Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems

In Recommender System (RS), explanations help users understand why items are recommended and can enhance a system's transparency, persuasiveness, engagement, and trust, which are known as explanation goals. However, evaluating the effectiveness of explanation algorithms offline remains challenging because explanation goals are inherently subjective. We initially conducted a rapid literature review, which revealed that algorithms are often assessed using anecdotal evidence (offering convincing examples) or using metrics that do not align with human perception. From these results, we investigated whether the selection of item attributes and interacted items affects explanation goals in explanations that generate a path connecting interacted and recommended items based on shared attributes (such as genres). We used metrics that measure the diversity and popularity of attributes and the recency of item interactions to evaluate explanations from three state-of-the-art agnostic algorithms across six recommendation systems. We then performed an online user study to compare user perceptions of explanation goals and offline metrics. Our findings indicate that engagement is sensitive to users' perceptions of diversity in explanations, whereas transparency, trust, and persuasiveness are influenced by perceptions of both popularity and diversity. However, offline metrics require refinement to more closely align with explanation goals and user understanding.

cs.IR↗