Exploring the Role of Local and Global Explanations in Recommender Systems

Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popovic, Daniel S Weld
2022 CHI Conference on Human Factors in Computing Systems Extended Abstracts  
Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining individual recommendations, or global, explaining the recommender model overall. Despite their widespread use, there has been little investigation into the relative benefts of the two explanation approaches. We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender to analyze how providing local, global, or both
more » ... xplanations infuences user understanding of system behavior. Our results provide evidence suggesting that both are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efciently identifying false positive and negative recommendations. However, we note that the two explanation approaches may be better compared in a higher-stakes or more opaque domain. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Machine learning; • Human-centered computing → Empirical studies in HCI.
doi:10.1145/3491101.3519795 fatcat:4sqgxlfbcffaddwxwucmngmquq