Personalized Persuasive Recommender System: A Framework and a Machine Learning‐Based Implementation
Bibliographic record
Abstract
ABSTRACT Since the emergence of Recommender Systems (RS), most of the research has focused on improving the accuracy of a recommender system. However, the literature has demonstrated increasing evidence that it is vital for a recommender system to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. Consequently, there becomes a need for new research paradigms to help improve the capabilities of recommender systems, which goes beyond recommendation accuracy. One of the recently emerged research directions that consider this need fosters the idea of adopting human‐related theories from the social sciences domain, such as persuasiveness of social communication. In this context, however, a challenging, non‐trivial, and not fully explored issue that arises is: how to integrate human‐related theories into a recommender system to increase user's acceptance ? This paper aims to address this issue by providing a reference architecture framework to adapt and integrate persuasion features as a substantial characteristic of recommender systems. The proposed framework, named Per sonalized Per suasive RS ( PerPer ), adopts concepts from the social sciences literature, namely personality traits and persuasion principles. This paper also introduces a machine learning‐based implementation of PerPer . In particular, it adapts the Learning Automata concepts to support learning capabilities. PerPer is evaluated using a user study where we implemented a prototype of a movie RS. The user study involved three parts, namely, the Conventional Recommender System (CRS) and two variants of PerPer that we called the General Reinforcement Approach (PerPer‐GRA) and the Boosted Reinforcement Approach (PerPer‐BRA). The analysis of the results obtained from 44 participants shows that PerPer was able to enhance users' acceptance of the recommendations in comparison to CRS. The results also show that the PerPer‐BRA outperforms the PerPer‐GRA in terms of accelerating the convergence of the best persuasion method while maintaining improvement in users' acceptance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".