A bias study and an unbiased deep neural network for recommender systems
Bibliographic record
Abstract
User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users’ behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".