Hybrid recommender systems based on autoencoders
Bibliographic record
Abstract
Due to the abundance of choice in e-commerce, recommender systems are becoming more and more indispensable.A common task of recommendation is to help users find the items that best fit their personal tastes.In the real world, most recommender services measure their performance by top-K item recommended to the users.In this thesis, we investigate the most common scenario (e.g.movies, products, CDs) with implicit feedback.There are many methods to solve the problem, including Collaborative filtering (CF).CF is a widely used approach in recommender systems based on past ratings given to items by users.However, CF-based methods suffer from cold-start problem because the ratings are often very sparse.To address this sparsity problem, more attention has been drawn to hybrid methods which utilize auxiliary information such as item content information.I would like to thank Professor Xue Liu for his expert advice and continuous support throughout my study period at McGill University.Without his help and support, I cannot meet such brilliant people and conduct such interesting research.I am sincerely grateful for his advice and help.Special thanks to Chen Ma
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".