An Adaptive Classification Framework for Handling the Cold Start Problem in Case of News Items
Bibliographic record
Abstract
Many recommendation systems make product or service suggestions based on existing knowledge of the user or the item. We must deal with two types of cold start problems: item-based and user-based cold start problems. In a user-based cold start dilemma, it is difficult for the system to suggest news to a new user whose information is not saved in the system. In this article, we attempt to address user based cold start problem by assuming that in the case of a user, we only know one type of information about the user, and that information is the user's location. Using BBC news data, an ID3 classification approach was developed, which incorporates eight explanatory factors such as News ID, News text, Keywords, date, Location, Shares count, followers, and so on. The classification accuracy of one of the best fit models constructed using (80-20)% training and test ratios is around 78%. Our technique is an effective tool for the cold-start problem because it outperforms the advice by a significant margin depending on the location. According to the results, our approach is competitive in terms of both accuracy and precision.
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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".