The Design and Implementation of a Business Intelligence Recommender
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, search engines perform a reasonable job in finding the necessary information according to the user's needs. Optimizing to the needs of the general population, these search engines quite often fail business individuals who seek enterprise information pertaining to a particular industry. The Web-based Intelligence Recommender for Enterprise (WIRE) is designed to assist these business individuals. The system uses existing Internet search engines as a basis to collect relevant documents via links encountered during crawling. The pages visited are analyzed and given a score according to certain criteria tailored to the individual's or the organization's interest. The score of a document thus represents its suitability and validity for business use. Selected pages are ranked and presented to the user for further actions. In order to adapt to the individuality of the user and the uniqueness of the type of business, design emphasis is placed on modularity and extensibility of the Recommender.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it