Time aware topic based recommender system
Bibliographic record
Abstract
News recommender systems efficiently handle the overwhelming number of news articles, simplify navigations, and retrieve relevant information. Many conventional news recommender systems use collaborative filtering to make recommendations based on the behavior of users in the system. In this approach, the introduction of new users or new items can cause the cold start problem, as there will be insufficient data on these new entries for the collaborative filtering to draw any inferences for new users or items. Content-based news recommender systems emerged to address the cold start problem. However, many content-based news recommender systems consider documents as a bag-of-words neglecting the hidden themes of the news articles. In this paper, we propose a news recommender system leveraging topic models and time spent on each article. We build an automated recommender system that is able to filter news articles and make recommendations based on users' preferences. We use topic models to identify the thematic structure of the corpus. These themes are incorporated into a content-based recommender system to filter news articles that contain themes that are of less interest to users and to recommend articles that are thematically similar to users' preferences. Our experimental studies show that utilizing topic modeling and spent time on a single article can outperform the state of the arts recommendation techniques. The resulting recommender system based on the proposed method is currently operational at The Globe and Mail (http://www.theglobeandmail.com/).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".