Adversarial Validation to Select Validation Data for Evaluating Performance in E-commerce Purchase Intent Prediction

Shotaro Ishihara, Shuhei Goda, and Hidehisa Arai. 2021. Adversarial Validation to Select Validation Data for Evaluating Performance in E-commerce Purchase Intent Prediction. In Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR eCom’21). ACM, New York, NY, USA, 5 pages.
https://github.com/upura/sigir-ecom-2021/

Transcript

1. Adversarial Validation to Select Validation Data for Evaluating Performance in E-commerce Purchase Intent Prediction Shotaro Ishihara (Nikkei, Inc.), Shuhei Goda (Wantedly, Inc.), Hidehisa Arai (Recruit Co., Ltd.) July 15th 2021, SIGIR eCom’21 Third place solution at Purchase Intent Prediction task in Coveo Data Challenge
2. Competition: Find as many positive samples as possible <- our team 2 <- (strong) baseline: all zero https://sigir-ecom.github.io/data-task.html
3. 2021-07-15 22:05:00, search 2021-07-15 22:05:30, view detail 2021-07-15 22:06:20, view detail 2021-07-15 22:07:00, search 2021-07-15 22:07:30, view detail 2021-07-15 22:07:50, add to cart 2021-07-15 ??:??:??, purchase or not Overview of Purchase Intent Prediction Tasks 3 ...... 2021-07-15 22:08:00, view detail 2021-07-15 22:09:30, search The number of browsing events (nb) after “add to cart” nb ∈ {0, 2, 4, 6, 8, 10} in test data
4. Solution Overview 4 2021-07-15 22:05:00, search 2021-07-15 22:05:30, view detail 2021-07-15 22:06:20, view detail 2021-07-15 22:07:00, search 2021-07-15 22:07:30, view detail 2021-07-15 22:07:50, add to cart Feature engineering -> LightGBM (nb ∈ {0, 2, 4, 6, 8, 10}) -> nb ∈ {0, 2, 4, 6, 8}: predict all samples as negative nb ∈ {10}: predict a few samples with high confident as positive by rank averaging of two models Transformer & LSTM (nb ∈ {0, 2, 4, 6, 8, 10})
5. Difficulties: - Train & test data were split by timeline. - Participants had to extract train data from the original data. - There was an extreme class imbalance. - Only total ten submissions were allowed for the final stage. Validation methodology: - Simple cross validation would not to be appropriate due to concept drift and class imbalance. Key Points 5
6. Cross Validation & Adversarial Validation 6 Cross validation: The data is divided into k folds; k-1 folds are used for training and the other fold is used for validation, which is done for all combinations. Adversarial validation: A binary classifier is trained to predict whether a sample belongs to test data or not. Training data highly similar to test data is sampled. Train Test Validation Hold out fold Cross validation fold fold fold
7. Our Validation Strategy 7 Test Validation Cross validation Train Train Train Train Validation Train Train Train Train Validation Train Train Train Train Validation Validation Validation Validation Validation Validation Adversarial validation Select validation data
8. Validation Results - Adversarial validation results told us the bigger nb model performed better. - Using nb==10 model led us to outperform the baseline. The other models didn’t work for us. - When we use all validation data (cross validation) and random selection (extract the same number of train data as the test data), we couldn’t get any insight which can be used for the submission. 8
9. - This paper described a methodology of using adversarial validation to select validation data for the evaluation of machine learning models. - We tackled the e-commerce purchase intent prediction task and the insight gained by the proposed methodology enabled us to outperform the baseline. - Source codes are available at https://github.com/upura/sigir-ecom-2021/. - ACM Reference Format: Shotaro Ishihara, Shuhei Goda, and Hidehisa Arai. 2021. Adversarial Validation to Select Validation Data for Evaluating Performance in E-commerce Purchase Intent Prediction. In Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR eCom’21). ACM, New York, NY, USA, 5 pages. Conclusion 9