SOURCE-LINKED INTELLIGENCE
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
In this reproducibility study, we investigate the transparency and scrutability of recommender systems enhanced by incorporating generated natural-language user profiles that represent user preferences. The original paper explores the synthesis of user profiles from raw user-generated review text across domains such as movies and accommodations (Amazon Movies & TV, TripAdvisor). Crucially, these natural-language user profiles enable direct user interaction and intervention, allowing users to customize recommendations by correcting misattributed preferences or addressing cold-start settings. We
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T07:36:34.000Z
- arXiv · Artificial Intelligence · 2026-09-17T07:36:34.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.