Robust Interaction-based Relevance Modeling for Online E-Commerce and LLM-based Retrieval

• URL: http://arxiv.org/abs/2406.02135v1
• Date: Tue, 4 Jun 2024 09:24:04 GMT
• Title: Robust Interaction-based Relevance Modeling for Online E-Commerce and LLM-based Retrieval
• Authors: Ben Chen, Huangyu Dai, Xiang Ma, Wen Jiang, Wei Ning,
• Abstract summary: Traditional text-matching techniques fail to capture the nuances of search intent accurately. We introduce a robust interaction-based modeling paradigm to address these shortcomings. To the best of our knowledge, this method is the first interaction-based approach for large e-commerce search relevance calculation.
• Score: 8.499253194630665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic relevance calculation is crucial for e-commerce search engines, as it ensures that the items selected closely align with customer intent. Inadequate attention to this aspect can detrimentally affect user experience and engagement. Traditional text-matching techniques are prevalent but often fail to capture the nuances of search intent accurately, so neural networks now have become a preferred solution to processing such complex text matching. Existing methods predominantly employ representation-based architectures, which strike a balance between high traffic capacity and low latency. However, they exhibit significant shortcomings in generalization and robustness when compared to interaction-based architectures. In this work, we introduce a robust interaction-based modeling paradigm to address these shortcomings. It encompasses 1) a dynamic length representation scheme for expedited inference, 2) a professional terms recognition method to identify subjects and core attributes from complex sentence structures, and 3) a contrastive adversarial training protocol to bolster the model's robustness and matching capabilities. Extensive offline evaluations demonstrate the superior robustness and effectiveness of our approach, and online A/B testing confirms its ability to improve relevance in the same exposure position, resulting in more clicks and conversions. To the best of our knowledge, this method is the first interaction-based approach for large e-commerce search relevance calculation. Notably, we have deployed it for the entire search traffic on alibaba.com, the largest B2B e-commerce platform in the world.

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