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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".