Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it