To explain or not to explain
Bibliographic record
Abstract
Recommender systems have been increasingly used in online services that we consume daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often presented to users as a "black box", i.e. the rationale for providing individual recommendations remains unexplained to users. In recent years, various attempts have been made to address this black box issue by providing textual explanations or interactive visualisations that enable users to explore the provenance of recommendations. Among other things, results demonstrated benefits in terms of precision and user satisfaction. Previous research had also indicated that personal characteristics such as domain knowledge, trust propensity and persistence may also play an important role on such perceived benefits. Yet, to date, little is known about the effects of personal characteristics on explaining recommendations. To address this gap, we developed a music recommender system with explanations and conducted an online study using a within-subject design. We captured various personal characteristics of participants and administered both qualitative and quantitative evaluation methods. Results indicate that personal characteristics have significant influence on the interaction and perception of recommender systems, and that this influence changes by adding explanations. For people with a low need for cognition are the explained recommendations the most beneficial. For people with a high need for cognition, we observed that explanations could create a lack of confidence. Based on these results, we present some design implications for explaining recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".