Exploring The Role of Local and Global Explanations in Recommender Systems
This work addresses the need for clearer explanation strategies in recommender systems, though it is incremental as it builds on existing research without introducing new methods.
The study investigated whether local and global explanations in recommender systems offer similar or distinct benefits to users, finding that combining both explanations improved understanding of how to enhance recommendations, while global explanations alone were more efficient for identifying errors.
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.