Case Based Reasoning for Information Personalization: Using a Context-Sensitive Compositional Case Adaptation Approach
Bibliographic record
Abstract
In this paper, we present an intelligent information filtering strategy that is a hybrid of item-based Collaborative Filtering (CF) and Case Based Reasoning (CBR) methods. Information filtering is implemented in two phases: in phase I, we have developed a multi-feature item-based CF strategy that allows creating a detailed context for filtering the information and retrieving N information objects based on user's interests and also preferred by similar users with similar tastes. In phase II, we use the N retrieved items as input to the CBR information filtering system and apply CBR-based compositional adaptation technique to selectively collect distinct information components of the N retrieved past items pairs to produce a composite recommendation that better addresses the initial user's interests and needs. We show that the hybrid of context-based similarity and compositional adaptation techniques improves significantly the quality of the recommendations presented to the user in terms of accurate and precise personalized information content.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".