ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction
| Autor: | Kabir, Md. Ahsanul, Hasan, Mohammad Al, Mandal, Aritra, Tunkelang, Daniel, Wu, Zhe |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the International Workshop on Interactive and Scalable Information Retrieval methods for eCommerce (ISIR-eCom) 2022 |
| Druh dokumentu: | Working Paper |
| Popis: | Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the same scale. However, to boost e-commerce platform's monetization, it is important to predict high-level similarity more accurately than low-level similarity, as highly similar queries retrieves items according to user-intents, whereas moderately similar item retrieves related items, which may not lead to a purchase. Regression models fail to customize its loss function to concentrate around the high-similarity band, resulting poor performance in query similarity prediction task. We address the above challenge by considering the query prediction as an ordinal regression problem, and thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction). ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values. We evaluate ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform and show that ORDSIM achieves substantially smaller prediction error compared to the competing regression methods on this dataset. Comment: 9 pages |
| Databáze: | arXiv |
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