EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search

• URL: http://arxiv.org/abs/2609.07143v1
• Date: Mon, 07 Sep 2026 07:44:02 GMT
• Title: EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search
• Abstract summary: E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed.<n>We propose EAGER, a two-stage framework for generating query suggestions from clicked items.<n>In the enrichment stage, supervised fine-tuning (SFT) follows a four-stage curriculum that scales information richness.<n>In the alignment stage, we post-train via GRPO with a hybrid reward of multiple rule-based business signals and a preference-aware click reward.
• Score: 5.7422000655776815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs -- limited to past behavior and blind to long-tail, personalized intents -- or rely on off-the-shelf LLMs whose lack of platform-specific knowledge yields fluent but generic queries disconnected from real click behavior. We propose EAGER (Enrich-and-AliGn gEnerative Query Recommendation), a two-stage framework for generating query suggestions from clicked items. In the enrichment stage, supervised fine-tuning (SFT) follows a four-stage curriculum that scales information richness (from item-only to user-conditioned) and reasoning depth (from direct to chain-of-thought). Each stage incorporates rationale augmentation, diversity regularization, and self-distillation. In the alignment stage, we post-train via GRPO with a hybrid reward of multiple rule-based business signals and a preference-aware click reward. Extensive offline experiments and online A/B test demonstrate the effectiveness of EAGER, which has been deployed in production at a major e-commerce platform.

Related papers

• One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation [0.8257831610561874]
  Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers.<n>We investigate whether a single hierarchical Semantic ID (sid) representation can support personalized ranking and query reformulation.
  arXiv  Detail & Related papers  (2026-08-21T00:27:32Z)
• Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce [33.55727546262202]
  CaLIR is a category-guided latent intent reasoning framework for e-commerce generative retrieval.<n>We introduce hierarchical semantic reasoning to align latent states with category-level shopping intent.<n>Experiments on multilingual e-commerce search datasets show that CaLIR achieves a better balance between retrieval effectiveness and inference efficiency than existing methods.
  arXiv  Detail & Related papers  (2026-06-05T09:12:54Z)
• SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding [64.45047674586671]
  We introduce the concept of an intention tree and propose a dataset curation pipeline.<n>We construct a sibling multimodal benchmark, SessionIntentBench, that evaluates L(V)LMs' capability on understanding inter-session intention shift.<n>With 1,952,177 intention entries, 1,132,145 session intention trajectories, and 13,003,664 available tasks mined using 10,905 sessions, we provide a scalable way to exploit the existing session data.
  arXiv  Detail & Related papers  (2025-07-27T09:04:17Z)
• OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion [22.63172274938755]
  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.
  arXiv  Detail & Related papers  (2025-06-07T20:24:05Z)
• EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association [83.4879773429742]
  This paper defines the task of E-commerce Script Planning (EcomScript) as three sequential subtasks.<n>We propose a novel framework that enables the scalable generation of product-enriched scripts by associating products with each step.<n>We construct the very first large-scale EcomScript dataset, EcomScriptBench, which includes 605,229 scripts sourced from 2.4 million products.
  arXiv  Detail & Related papers  (2025-05-21T07:21:38Z)
• Generative Product Recommendations for Implicit Superlative Queries [21.750990820244983]
  In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as "best shoes for trail running"<n>We investigate how Large Language Models can generate implicit attributes for ranking as well as reason over them to improve product recommendations for such queries.
  arXiv  Detail & Related papers  (2025-04-26T00:05:47Z)
• Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap [1.2356255208135267]
  PINCER transforms initial user queries into pseudo-product representations.<n>We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval.
  arXiv  Detail & Related papers  (2025-01-21T23:47:39Z)
• Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation [71.5871100348448]
  ColdGPT models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins.
  arXiv  Detail & Related papers  (2023-06-26T07:04:47Z)
• U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation [59.81301478480005]
  We construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledges.
  arXiv  Detail & Related papers  (2023-05-05T01:44:35Z)
• 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)
• Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling [62.049330405736406]
  Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant. It is insufficient to provide the same "completely summarized" answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves. We propose a novel multi-perspective user preference model for generating personalized answers in PQA.
  arXiv  Detail & Related papers  (2021-12-27T07:51:49Z)

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.