Digitally nudging users to explore off-profile recommendations: here be dragons
Bibliographic record
Abstract
Abstract In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping themdiscovernew types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extentdigital nudging, i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ N=1064 ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".