Bibliographic record
Abstract
The primary objective of recommender systems, in a general sense, is to recommend items to users rather than to persuade users to get those items. Hence, a recommender system is not a persuasive technology by itself. In recent years, however, there has been increasing attention in the literature towards augmenting persuasiveness features into recommender systems. Several researchers have discussed the feasibility of enriching recommendations with persuasive messages. Nonetheless, there is a lack of works that discuss how to incorporate personalized and dynamic persuasive capabilities to recommender systems. To mitigate this issue, we propose the Personalized Persuasive RS (PerPer) framework. The PerPer adopts learning automata concepts to dynamically choose a suitable persuasive strategy for users in a personalized manner. PerPer is general enough to be plugged into different recommenders and to consider several persuasive strategies. PerPer aims to provide a simple and straightforward way to incorporate persuasive features to recommenders. By this, it would have the potential of increasing users' perceived acceptance of 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.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".