A Time-Window Approach to Recommending Emerging and On-the-rise Items
Bibliographic record
Abstract
The recommendation system (RS) filters large sales transaction data, to promote item Y as an alternative or pair to item X that the application user is looking for. Items that are no longer in season usually decrease in transactions, even to zero. Constantly recommending these items is irrelevant, while other items that are more relevant and profitable are not recommended, such as the emerging and on-the-rise items. The proposed solution to this problem is the time window approach, which is a block of data of a certain size and recorded at a certain time stamp. Item combinations (itemsets) are mined from each window with an association rule approach, and changes in the number of transactions containing these items are evaluated from window to window. From the number of transactions that contain the itemsets, the system distinguishes status items into four types: risky, normal, emerging, and on-the-rise. Items that are currently suffering from zero sales risk the same fate in the next window, so they are called risky items. The system will notify admins to review this item, and look for other emerging or on-the-rise items to promote along with item X. Experiments were carried out on two datasets, and it was found that 42.7% and 59.4% items from each dataset are risky.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".