AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online

• URL: http://arxiv.org/abs/2003.11941v5
• Date: Thu, 31 Dec 2020 10:04:48 GMT
• Title: AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
• Authors: Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qing Da, An-Xiang Zeng, Han Yu, and Yang Yu, and Zhi-Hua Zhou
• Abstract summary: This paper proposes an evaluator-generator framework for learning-to-rank. It consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning. Our method achieves a significant improvement in terms of Conversion Rate (CR) over the industrial-level fine-tuned model in online A/B tests.
• Score: 60.887637616379926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collected from the online system. However, it has been noticed that previous LTR models can have a good validation performance over offline validation data but have a poor online performance, and vice versa, which implies a possible large inconsistency between the offline and online evaluation. We investigate and confirm in this paper that such inconsistency exists and can have a significant impact on AliExpress Search. Reasons for the inconsistency include the ignorance of item context during the learning, and the offline data set is insufficient for learning the context. Therefore, this paper proposes an evaluator-generator framework for LTR with item context. The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator. Extensive experiments in simulation environments and AliExpress Search online system show that, firstly, the classic data-based metrics on the offline dataset can show significant inconsistency with online performance, and can even be misleading. Secondly, the proposed evaluator score is significantly more consistent with the online performance than common ranking metrics. Finally, as the consequence, our method achieves a significant improvement (\textgreater$2\%$) in terms of Conversion Rate (CR) over the industrial-level fine-tuned model in online A/B tests.

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