Personalized recommendation, behavior-based pricing, or both? Examining privacy concerns from a cost perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the era of the big data, e-commerce increasingly adopts personalized recommendation and behavior-based pricing (BBP) strategies to enhance consumer experience, while also raising concerns about privacy. This study examines the impact of privacy costs on the effectiveness of those strategies using a two-period Hotelling model. The results indicate that retailers who combine personalized recommendation with BBP strategies can achieve higher prices and profits compared to those who do not employ these strategies, particularly when there are significant differences in privacy costs. Our study further reveals that relying solely on personalized recommendation without incorporating BBP may lead to decreases profit. Moreover, the accuracy of recommendations and variations in privacy costs significantly influence retailers’ strategy choices, emphasizing the importance of these factors in gaining a competitive advantage. This research provides valuable insights for online retailers on how to effectively position themselves in the market while addressing consumer privacy concerns, offering a new perspective on the comprehensive impacts of personalized recommendation and BBP strategies in the business landscape. • Impacts of privacy costs on the effectiveness of strategies are studied. • Retailers who combine personalized recommendation with BBP achieve higher profits. • Relying on personalized recommendation without BBP may lead to decrease profit. • The accuracy of recommendations and privacy costs affect strategy choices. • The expanding gap in privacy cost intensifies market competition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 it