Pyramid collaborative filtering technique for an intelligent autonomous guide agent
Bibliographic record
Abstract
This article presents an autonomous guide agent that can observe a community of learners on the web, interpret the learners' inputs, and then assess their sharing. The goal of this agent is to find a reliable helper (tutor or other learner) to assist a learner in solving his task. Despite the growing number of Internet users, the ability to find helpers is still a challenging and important problem. Although helpers could have much useful information about courses to be taught, many learners fail to understand their presentations. For that, the agent must be able to deal autonomously with the following challenges: Do helpers have information that the learners need? Will helpers present information that learners can understand? And can we guarantee that these helpers will collaborate effectively with learners? We have developed a new filtering framework, called a pyramid collaborative filtering model, to whittle the number of helpers down to just one. We have proposed four levels for the pyramid. Moving from one level to another depends on three filtering techniques: domain model filtering, user model filtering, and credibility model filtering. A new technique is filtering according to helpers' credibilities. Our experiments show that this method greatly improves filtering effectiveness. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1065–1082, 2007.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".