HELP: A Recommender System to Locate Expertise in Organizational Memories
Bibliographic record
Abstract
The rapid evolution of our world means that learning and knowledge sharing are fast becoming a key challenge for individuals and organizations. In this paper, we present a system called HELP, whose aim is to locate information and recommend experts in organizations. Each user is being viewed simultaneously as an expert and a learner. We use two approaches: The first one consists of making the system retrieve one or several requests similar to the seeking-learner's request using a textual case-based reasoning technique. The second approach aims at locating experts in specific areas in order to recommend them to the users who request this expertise. For this purpose, we use a hybrid recommendation technique based on Collaborative Filtering (CF) and Case-Based Reasoning (CBR). In contrast to existing approaches in expertise location, we believe that CBR combined to CF enables HELP to better recommend expertise, taking into account the user's feedback concerning the technical and pedagogical skills of the experts.
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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 | 0.001 | 0.000 |
| 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".