Robust framework for the joint learning of consumer preferences and market segmentation
Bibliographic record
Abstract
Learning consumer preferences is essential to maximize profits. To optimize the product line, accurately segmenting the market and eliciting consumer preferences in each segment are critically important. We present a robust framework to simultaneously segment the customer base and learn each segment’s preferences. We build upon ideas from machine learning and mathematical programming and propose a robust preference elicitation model. Our model accounts for robustness against feature noise (i.e., perturbations caused by consumers inaccurately comparing alternatives), and handles label noise (i.e., inconsistent consumer choices) using a weighting scheme that determines the relevance of the past choices in predicting future ones. The proposed framework has three appealing characteristics. First, it simultaneously segments the market and learns the segments’ preferences. Second, it extends an ML-based preference learning method that has been proven to be effective. Third, the decision maker can choose the level of robustness and has the option to focus on the parsimony of the solution. We perform extensive experiments and show that the proposed framework offers better prediction accuracy and lower variability in the predictions.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".