OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion

• URL: http://arxiv.org/abs/2506.06913v1
• Date: Sat, 07 Jun 2025 20:24:05 GMT
• Title: OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion
• Authors: Xian Guo, Ben Chen, Siyuan Wang, Ying Yang, Chenyi Lei, Yuqing Ding, Han Li,
• Abstract summary: OneSug is an end-to-end generative framework for e-commerce query suggestion.<n>OneSug has been successfully deployed for the entire traffic on the e-commerce search engine in Kuaishou platform for over 1 month.
• Score: 22.63172274938755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Query suggestion plays a crucial role in enhancing user experience in e-commerce search systems by providing relevant query recommendations that align with users' initial input. This module helps users navigate towards personalized preference needs and reduces typing effort, thereby improving search experience. Traditional query suggestion modules usually adopt multi-stage cascading architectures, for making a well trade-off between system response time and business conversion. But they often suffer from inefficiencies and suboptimal performance due to inconsistent optimization objectives across stages. To address these, we propose OneSug, the first end-to-end generative framework for e-commerce query suggestion. OneSug incorporates a prefix2query representation enhancement module to enrich prefixes using semantically and interactively related queries to bridge content and business characteristics, an encoder-decoder generative model that unifies the query suggestion process, and a reward-weighted ranking strategy with behavior-level weights to capture fine-grained user preferences. Extensive evaluations on large-scale industry datasets demonstrate OneSug's ability for effective and efficient query suggestion. Furthermore, OneSug has been successfully deployed for the entire traffic on the e-commerce search engine in Kuaishou platform for over 1 month, with statistically significant improvements in user top click position (-9.33%), CTR (+2.01%), Order (+2.04%), and Revenue (+1.69%) over the online multi-stage strategy, showing great potential in e-commercial conversion.

Related papers

• SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation [14.449201436664692]
  We propose a Semantic-Behavior Alignment for Cold-start Recommendation framework.<n>First, we leverage domain-specific knowledge to train a multimodal encoder to generate semantic representations.<n>Second, we use residual quantized semantic ID to dynamically bridge the gap between multimodal representations and the ranking model.
  arXiv  Detail & Related papers  (2025-08-02T14:09:21Z)
• MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System [1.5960546024967321]
  MOHPER is a multi-objective optimization framework for e-commerce sites.<n>It jointly optimize click-through rate (CTR), click-through conversion rate (CTCVR) and relevant objectives.<n>It substantiates its practical efficacy in achieving a balanced optimization that aligns with both user satisfaction and revenue goals.
  arXiv  Detail & Related papers  (2025-03-07T08:25:08Z)
• Centrality-aware Product Retrieval and Ranking [14.710718676076327]
  This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. We curate samples from eBay, manually annotated with buyer-centric relevance scores and centrality scores, which reflect how well the product title matches the users' intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimises for the user intent in semantic product search.
  arXiv  Detail & Related papers  (2024-10-21T11:59:14Z)
• Enhancing DETRs Variants through Improved Content Query and Similar Query Aggregation [27.07277433645018]
  We introduce a novel plug-and-play module, Self-Adaptive Content Query (SACQ) SACQ generates content queries via self-attention pooling. It allows candidate queries to adapt to the input image, resulting in a more comprehensive content prior and better focus on target objects. We propose a query aggregation strategy to cooperate with SACQ. It merges similar predicted candidates from different queries, easing the optimization.
  arXiv  Detail & Related papers  (2024-05-06T09:50:04Z)
• Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations [76.70349332096693]
  Information-seeking dialogue systems are widely used in e-commerce systems. We propose a Query-bag based Pseudo Relevance Feedback framework (QB-PRF) It constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations.
  arXiv  Detail & Related papers  (2024-03-22T08:10:32Z)
• Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations [50.03560306423678]
  We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems. Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
  arXiv  Detail & Related papers  (2024-01-12T15:26:40Z)
• Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model [78.80174696043021]
  We propose a novel model called the Entity-Based Relevance Model (EBRM) The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
  arXiv  Detail & Related papers  (2023-07-01T15:44:53Z)
• Recommender Systems with Generative Retrieval [58.454606442670034]
  We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
  arXiv  Detail & Related papers  (2023-05-08T21:48:17Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)
• Conditional Sequential Slate Optimization [15.10459152219771]
  A search ranking system typically orders the results by independent query-document scores to produce a slate of search results. We introduce conditional sequential slate optimization (CSSO), which jointly learns to optimize for traditional ranking metrics as well as prescribed distribution criteria of documents within the slate. The proposed method can be applied to practical real world problems such as enforcing diversity in e-commerce search results, mitigating bias in top results and personalization of results.
  arXiv  Detail & Related papers  (2021-08-12T09:14:46Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.