A Peer-to-Peer Collaborative Virtual Environment for E-Commerce
Bibliographic record
Abstract
The current e-commerce systems consist of a catalogue which online customers browse through, trying to emulate the real-life shopping experience. These customers, most of the times, are exposed to the product specifications in addition to some pictures and sometimes, animations. They do not undergo the same experience they would in reality: people often shop in groups and share opinions among each other and with product experts about items they browse before committing to purchase. All the pre-purchase experience is heavily undermined in the current electronic shopping emulation, potentially leading to reduced purchasing. However, this experience can be enhanced through Collaborative Virtual Environments (CVE). In this paper, we present a web-based e-commerce system where customers can collaboratively experience shopping with their online friends in real-time, and share the interaction with three dimensional virtual models of the items they are considering to buy available in the virtual shop. The system considers accessibility, a concern for any e-commerce application trying to attract as many customers as possible, and hence uses Macromedia Shockwave at the client side: a widely deployed free player. This differentiates our system from similar VR-based e-commerce systems that require VRML plug-ins or other nonstandard software at the client side. Another characteristic of the proposed system is its unique approach to supporting scalability. Realizing that collaborative shopping might lead to an increase in the number of shoppers browsing products simultaneously, scalability is an inevitable issue to deal with. We propose a peer-to-peer (P2P) architecture, where all peers provide resources and contribute in handling the scalability problem. The paper therefore focuses on two aspects: collaborative virtual environments in an e-commerce environment, and the application of peer-to-peer networking to such systems. Proof of concept and performance evaluations are also presented.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".