Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System

TL;DR

This paper introduces an interactive, user-centered explanation model for recommender systems that offers varying levels of detail, allowing users to personalize explanations, which improves transparency, trust, and satisfaction.

Contribution

It proposes a novel interactive explanation approach with multiple detail levels and demonstrates its positive impact through a user study in a real recommendation system.

Findings

Interactive explanations increase user trust and satisfaction.

Allowing users to choose explanation detail levels meets diverse user needs.

User control enhances perceived transparency and experience.

Abstract

Explainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N=14) to investigate the impact of providing interactive explanations with varying level of details on the users' perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience.