Text relevance or text matching of query and product is an essential technique for the e-commerce search system to ensure that the displayed products can match the intent of the query. Many studies focus on improving the performance of the relevance model in search system. Recently, pre-trained language models like BERT have achieved promising performance on the text relevance task. While these models perform well on the offline test dataset, there are still obstacles to deploy the pre-trained language model to the online system as their high latency. The two-tower model is extensively employed in industrial scenarios, owing to its ability to harmonize performance with computational efficiency. Regrettably, such models present an opaque ''black box'' nature, which prevents developers from making special optimizations. In this paper, we raise deep Bag-o f-Words (DeepBoW) model, an efficient and interpretable relevance architecture for Chinese e-commerce. Our approach proposes to encode the query and the product into the sparse BoW representation, which is a set of word-weight pairs. The weight means the important or the relevant score between the corresponding word and the raw text. The relevance score is measured by the accumulation of the matched word between the sparse BoW representation of the query and the product. Compared to popular dense distributed representation that usually suffers from the drawback of black-box, the most advantage of the proposed representation model is highly explainable and interventionable, which is a superior advantage to the deployment and operation of online search engines. Moreover, the online efficiency of the proposed model is even better than the most efficient inner product form of dense representation. The proposed model is experimented on three different datasets for learning the sparse BoW representations, including the human-annotation set, the search-log set and the click-through set. Then the models are evaluated by experienced human annotators. Both the auto metrics and the online evaluations show our DeepBoW model achieves competitive performance while the online inference is much more efficient than the other models. Our DeepBoW model has already deployed to the biggest Chinese e-commerce search engine Taobao and served the entire search traffic for over 6 months.

Product search is an important service on Taobao, the largest e-commerce platform in China. Through this service, users can easily find products relevant to their specific needs. Coping with billion-size query loads, Taobao product search has traditionally relied on classical term-based retrieval models due to their powerful and interpretable indexes. In essence, efficient retrieval hinges on the proper storage of the inverted index. Recent successes involve reducing the size (pruning) of the inverted index but the construction and deployment of lossless static index pruning in practical product search still pose non-trivial challenges. In this work, we introduce a novel SM art INDexing (SMIND) solution in Taobao product search. SMIND is designed to reduce information loss during the static pruning process by incorporating user search preferences. Specifically, we first construct "user-query-item'' hypergraphs for four different search preferences, namely purchase, click, exposure, and relevance. Then, we develop an efficient TermRank algorithm applied to these hypergraphs, to preserve relevant items based on specific user preferences during the pruning of the inverted indexer. Our approach offers fresh insights into the field of product search, emphasizing that term dependencies in user search preferences go beyond mere text relevance. Moreover, to address the vocabulary mismatch problem inherent in term-based models, we also incorporate an multi-granularity semantic retrieval model to facilitate semantic matching. Empirical results from both offline evaluation and online A/B tests showcase the superiority of SMIND over state-of-the-art methods, especially in commerce metrics with significant improvements of 1.34% in Pay Order Count and 1.50% in Gross Merchandise Value. Besides, SMIND effectively mitigates the Matthew effect of user queries and has been in service for hundreds of millions of daily users since November 2022.

Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and exert an indirect, yet substantial impact on business conversion. Large Language Models (LLMs) enable generative, reasoning-based approaches, typically aligned via supervised fine-tuning (SFT) or preference optimization methods like Direct Preference Optimization (DPO). However, the increasing complexity of business rules and user queries exposes the inability of existing methods to endow models with robust reasoning capacity for long-tail and challenging cases. Efforts to address this via reinforcement learning strategies like Group Relative Policy Optimization (GRPO) often suffer from sparse terminal rewards, offering insufficient guidance for multi-step reasoning, which in turn slows convergence. To address these challenges, we propose TaoSR-AGRL, an Adaptive Guided Reinforcement Learning framework for LLM-based relevance prediction in Taobao Search Relevance. TaoSR-AGRL introduces two key innovations: (1) Rule-aware Reward Shaping, which decomposes the final relevance judgment into dense, structured rewards aligned with domain-specific relevance criteria; and (2) Adaptive Guided Replay, which identifies low-accuracy rollouts during training and injects targeted ground-truth guidance to steer the policy away from stagnant, rule-violating reasoning patterns toward compliant trajectories. TaoSR-AGRL was evaluated on large-scale datasets and through online evaluations on Taobao Search. It consistently outperforms DPO and GRPO baselines, improving relevance accuracy and rule adherence, with measurable gains in user engagement and stable training dynamics. The model has been deployed on Taobao, serving hundreds of millions of users.