Research map / Reinforcement learning / Offline RL
Surveys without Questions: A Reinforcement Learning Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use lag(2) rather than lag(1) changes in proxy ratings for survey classification | Proxy-rating validation · Survey validation, test data | Accuracy: 0.6274 -> 0.6633 | Helped |
| Use lag(2) rather than lag(1) changes in proxy ratings for survey classification | Proxy-rating validation · Survey validation, test data | F1-Score: 0.7069 -> 0.7417 | Helped |
| Predict journey purchase using the proportion of good proxy ratings instead of customer survey scores | Journey-level purchase prediction · Purchase prediction | AUC: 0.51 -> 0.73 | Helped |
| Move from Customize to ProductDetail rather than ProductCategory | Proxy ratings · Customize transitions, purchase journeys | Proportion of Good Ratings: 0.44 -> 0.77 | Helped |
| Move from Home to ProductCategory rather than directly to ProductDetail | Proxy ratings · Purchase journeys | Proportion of Good Ratings: 0.16 -> 0.63 | Helped |
| Move from Home to ProductCategory rather than directly to ProductDetail | Proxy ratings · No-purchase journeys | Proportion of Good Ratings: 0.22 -> 0.68 | Helped |
| Use lag(2) rather than lag(1) changes in proxy ratings for survey classification | Proxy-rating validation · Survey validation, test data | Recall: 0.7408 -> 0.7967 | Helped |
| Use lag(2) rather than lag(1) changes in proxy ratings for survey classification | Proxy-rating validation · Survey validation, test data | Precision: 0.6760 -> 0.6938 | Helped |
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