Accessibility and scalability in collaborative eCommerce environments
Bibliographic record
Abstract
The Much advancement has lately occurred in eCommerce systems’ interfaces. Product specifications listings combined with pictures is no longer considered the benchmark for eCommerce interfaces. Albeit commercial websites haven’t 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 paper, we build on a collaborative eCommerce system. The system adopts concepts from virtual environments, allowing customers to interact with three-dimensional models of the items of interests 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 VRML viewers. The system also addresses scalability, by using a peer-to-peer communications architecture to support a number of geographically dispersed users on the Internet simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".