Exploring The Role of Local and Global Explanations in Recommender Systems

TL;DR

This study investigates how local and global explanations in recommender systems affect user understanding and efficiency, revealing that combining explanations improves understanding but may reduce efficiency in identifying errors.

Contribution

It provides the first comparative analysis of local and global explanations' effects on user understanding and efficiency in recommender systems.

Findings

Both explanations improve user understanding more than either alone.

Global explanations alone are more efficient for identifying false positives and negatives.

Combining explanations enhances understanding but may reduce efficiency in error detection.

Abstract

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining an individual recommendation, or global, explaining the recommender model in general. Despite their widespread use, there has been little investigation into the relative benefits of these two approaches. Do they provide the same benefits to users, or do they serve different purposes? We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender system to analyze how providing participants local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both explanations are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiency in identifying false positives and negatives. However, we note that the two explanation approaches may be better compared in the context of a higher-stakes or more opaque domain.