Complicated Semantic Alignment for Long-Tail Query Rewriting in Taobao Search Based on Large Language Model

In the realm of e-commerce search, semantic matching has consistently been a core issue, as it directly affects user experience and company revenue. However, users' queries often fail to effectively retrieve relevant products due to discrepancies between the user's expression habits and product names written by merchants. Even existing large language model (LLM) based query rewriting methods can bridge the semantic gap for most queries, they are still ineffective for long-tail queries with complicated semantic. In this paper, we propose Complicated Semantic Alignment Query Rewrite(CSA-QR) framework, which mitigates the semantic differences in long-tail queries with complicated semantics. CSA-QR comprises three stages: high-quality supervised fine-tuning (SFT) dataset generation, multi-dimensional alignment dataset generation, and binary feedback Proximal Policy Optimization (PPO) for reinforcement alignment. Initially, we utilize general large language models to generate rewrite candidates, followed by manual annotation to discriminate the candidates, then use the retrieval augmentation generation (RAG) based on existing annotations to produce a higher quality SFT dataset. Subsequently, we decouple the feedback data into user semantic consistency and merchant expression consistency dimensions to collect multi-dimensional alignment data. Finally, we introduce a binary feedback method to train the reward model, enabling it to better guide alignment training within our context. We also identify a set of more appropriate reward model evaluation metrics to guide our iterations. Offline experiments demonstrate the effectiveness of this method in improving retrieval performance. Online A/B tests reveal that our method significantly boosts critical metrics such as product click-through rate (CTR), gross merchandise volume (GMV) and number of transaction (#Trans) for long-tail complicated queries. CSA-QR has been deployed on Taobao, one of China's most popular online shopping platforms, since September 2024.

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Published
2025-08-03
DOI
https://doi.org/10.1145/3711896.3737204
Primary Topic
Topic Modeling
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article
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article

Complicated Semantic Alignment for Long-Tail Query Rewriting in Taobao Search Based on Large Language Model

Yajing Xu, Yunling Feng, Dan Ou, Huang Jian-feng et al.
Topic Modeling
article

Complicated Semantic Alignment for Long-Tail Query Rewriting in Taobao Search Based on Large Language Model

Yajing Xu, Yunling Feng, Dan Ou, Huang Jian-feng, Ling Gui, Yue Jiang, Fuyu Lv, Quan Liu
article en

Abstract

In the realm of e-commerce search, semantic matching has consistently been a core issue, as it directly affects user experience and company revenue. However, users' queries often fail to effectively retrieve relevant products due to discrepancies between the user's expression habits and product names written by merchants. Even existing large language model (LLM) based query rewriting methods can bridge the semantic gap for most queries, they are still ineffective for long-tail queries with complicated semantic. In this paper, we propose Complicated Semantic Alignment Query Rewrite(CSA-QR) framework, which mitigates the semantic differences in long-tail queries with complicated semantics. CSA-QR comprises three stages: high-quality supervised fine-tuning (SFT) dataset generation, multi-dimensional alignment dataset generation, and binary feedback Proximal Policy Optimization (PPO) for reinforcement alignment. Initially, we utilize general large language models to generate rewrite candidates, followed by manual annotation to discriminate the candidates, then use the retrieval augmentation generation (RAG) based on existing annotations to produce a higher quality SFT dataset. Subsequently, we decouple the feedback data into user semantic consistency and merchant expression consistency dimensions to collect multi-dimensional alignment data. Finally, we introduce a binary feedback method to train the reward model, enabling it to better guide alignment training within our context. We also identify a set of more appropriate reward model evaluation metrics to guide our iterations. Offline experiments demonstrate the effectiveness of this method in improving retrieval performance. Online A/B tests reveal that our method significantly boosts critical metrics such as product click-through rate (CTR), gross merchandise volume (GMV) and number of transaction (#Trans) for long-tail complicated queries. CSA-QR has been deployed on Taobao, one of China's most popular online shopping platforms, since September 2024.

Alibaba Group (China) (CN)
Openalex Percentile: Top 90%
Topic Modeling
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