Toward Deep Understanding of Persuasive Product Recommendation Agents
Bibliographic record
Abstract
Product recommendation agents (PRA) are systems built to facilitate customersâ products purchase on e-commerce websites. Prior literature focuses on the âshapingâ effects of PRA to customersâ decision making. More challengingly, PRA can be built to change customersâ product choice by combining with persuasive features. This paper explores this new type of PRA âpersuasive product recommendation agentsâ (PPRA). In this paper, we make a distinction of PPRA with neutral and deceptive ones. The basic functioning principle of PPRA is stated and a classification of persuasive tactics is made. We propose the mechanism via which PPRA work by incorporating elaboration likelihood model, 4w and theory of reasoned action together. Despite marketing usage, the proposed PPRA can be used to benefit society by promoting green purchases or encouraging charity. The theory also has the generalizability to be used in decision making contexts like healthcare and education. Discussion and future research directions are made.
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How this classification was reachedexpand
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 | 0.005 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".