Towards Improving E-commerce Users Experience Using Personalization & Persuasive Technology
Bibliographic record
Abstract
With the increase in the number of e-commerce companies over the last decade, there is stiffer competition for e-businesses to win and retain customers. Companies have to give clients reasons to shop with them and become return customers. The use of personalization strategies and persuasive technology have been identified as means through which e-businesses can engage their clients and give them a unique shopping experience. To con-tribute to ongoing research in personalization and persuasive technology in e-commerce, my thesis proposes a framework that can create a personalized shopping experience for clients using the consumers' personality and shopping type. This paper presents the results of the first stage of my research which is a user study carried out on 324 e-commerce shoppers to identify the persuasive strategies and its implementation in an e-commerce site, Amazon, and to evaluate the persuasiveness of these strategies to consumers. The result of this thesis can contribute to ongoing research in development of personalization and persuasive strategies that work in e-commerce especially for new companies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".