The virtual boutique: a synergic approach to virtualization,content-base management of 3D information, 3D data mining an virtual reality for ecommerce
Bibliographic record
Abstract
The Virtual Boutique is an e-commerce environment based on virtual reality and content-based management of three-dimensional information. The boutique and its content are virtualized with laser scanners and photogrammetric techniques. Algorithms for searching the boutique inventory by three-dimensional shape and image content are described. Business rules are data mined from the interaction of the consumer with the boutique in a virtual environment. systems capable of performing modeling, design, animation and virtualization in three-dimension. Furthermore, the movie industry has favored the synergy between three- dimensional technology, advertising, art and business: a sine qua non condition for the application of three-dimensional technologies in the e-business field. The second major event was the advent of the game industry. Computer games are part of the life of most children and teenagers. Nowadays, most of these games are in three-dimension, which means that three-dimensional concepts are becoming natural and intuitive. Furthermore, the game industry has favored the development of affordable and high performance graphic cards. This means that most personal computers are now equipped with the right hardware for handling three-dimensional scenes in real time: at least up to a certain resolution. This paper describes the Virtual Boutique, an integrated environment based on virtual reality and on content-based management of visual information. The paper is organized as follows. Section II provides an overview of the current standards and discusses the importance of realistic rendering of objects in an e-commerce environment. This is followed, in Section III and IV, by a description of our system for three-dimensional object description and retrieval. Section V discusses the approach followed in order to describe pictures and texture. Section VI provides an overview of the Virtual Boutique, a virtual realty-based environment that integrates the methods described in the previous three sections. In Section VII, it is shown how the Virtual Boutique can be used for data mining. Finally, Section VIII concludes the paper.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".