Accessibility and scalability in collaborative e-commerce environments
Bibliographic record
Abstract
Much advancement has recently occurred in e-commerce systems' interfaces. Product specifications listings combined with pictures are no longer considered the benchmark for e-commerce interfaces. Albeit commercial websites have not ventured in these developments, academic research has tried, through this progression, to mimic the real-life shopping experience. Shopping in real-life is a social experience with other components attached to it: customers consult with experts and shop in groups benefiting from others' opinions. These aspects, when lacking, can lead to reduction in sales. In this article, we build on a collaborative e-commerce system. The system adopts concepts from virtual environments, allowing customers to interact with 3D models of the items of interest in the virtual shop as well as share those items with other customers or ask for expert opinions, in real-time. Our system addresses accessibility, by using Macromedia Shockwave, a widely deployed player, and therefore avoids the need of unusual plug-ins, such as Virtual Reality Modelling Language viewers. In addition, the system addresses scalability, by using a peer-to-peer communications architecture to support a number of geographically dispersed customers on the internet simultaneously.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".