Search Intenion Network for Personalized Query Auto-Completion in E-Commerce

• URL: http://arxiv.org/abs/2403.02609v1
• Date: Tue, 5 Mar 2024 02:53:24 GMT
• Title: Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
• Abstract summary: QAC systems face two major challenges:1)intention equivocality(IE): during the user's typing process,the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model. Previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer.
• Score: 23.839760520476744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process,the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model.2)intention transfer (IT):previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer.However, the current intention extracted from prefix may be contrary to the historical preferences.

Related papers

• Policy-Grounded Dynamic Facet Suggestions for Job Search [8.820401111478139]
  At LinkedIn, over 80% of job-related queries contain three or fewer keywords.<n>We present dynamic facet suggestion (DFS), an interactive query refinement mechanism that facilitates intent disambiguation.
  arXiv  Detail & Related papers  (2026-05-15T17:30:45Z)
• Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History [8.085230376705887]
  Persona2Web is the first benchmark for evaluating personalized web agents on the real open web.<n>It consists of: (1) user histories that reveal preferences implicitly over long time spans, (2) ambiguous queries that require agents to infer implicit user preferences, and (3) a reasoning-aware evaluation framework that enables fine-grained assessment of personalization.
  arXiv  Detail & Related papers  (2026-02-19T01:54:26Z)
• GenCI: Generative Modeling of User Interest Shift via Cohort-based Intent Learning for CTR Prediction [84.0125708499372]
  We propose a generative user intent framework to model user preferences for click-through rate (CTR) prediction.<n>The framework first employs a generative model, trained with a next-item prediction objective, to proactively produce candidate interest cohorts.<n>A hierarchical candidate-aware network then injects this rich contextual signal into the ranking stage, refining them with cross-attention to align with both user history and the target item.
  arXiv  Detail & Related papers  (2026-01-26T08:15:04Z)
• LEAPS: An LLM-Empowered Adaptive Plugin for Taobao AI Search [17.074638179635613]
  We propose LEAPS (LLM-Empowered Adaptive for Taobao AI Search), which seamlessly upgrades traditional search systems via a "Broaden-and-Empower" paradigm.<n>It employs a novel three-stage training strategy--inverse data augmentation, posterior-knowledge supervised fine-tuning, and diversity-aware reinforcement learning--to generate adaptive and complementary query combinations.<n>Fully deployed on Taobao AI Search since August 2025, LEAPS currently serves hundreds of millions of users monthly.
  arXiv  Detail & Related papers  (2026-01-09T03:41:27Z)
• A Generative Framework for Personalized Sticker Retrieval [73.57899194210141]
  We propose PEARL, a novel generative framework for personalized sticker retrieval.<n>We make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations, and (ii) To generate stickers aligned with a user's query intent, we propose a novel intent-aware learning objective.<n> Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-22T13:11:44Z)
• NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search [108.42163676745085]
  We envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search.<n> NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences.
  arXiv  Detail & Related papers  (2025-05-20T17:59:13Z)
• MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment [16.10791252542592]
  Motivation-Aware Personalized Search aims to retrieve and rank items that match users' preferences and search intent. Our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before searching. To address these, we propose a Motivation-Aware Personalized Search (MAPS) method.
  arXiv  Detail & Related papers  (2025-03-03T16:24:36Z)
• Tip of the Tongue Query Elicitation for Simulated Evaluation [49.083870362622164]
  Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. Current approaches rely heavily on community question-answering (CQA) websites, leading to labor-intensive evaluation and domain bias. We introduce two methods for eliciting TOT queries - leveraging large language models (LLMs) and human participants.
  arXiv  Detail & Related papers  (2025-02-25T02:11:42Z)
• AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset [14.544120039123934]
  We introduce AmazonQAC, a new QAC dataset sourced from Amazon Search logs, comprising 395M samples. The dataset includes actual sequences of user-typed prefixes leading to final search terms, as well as session IDs and timestamps. We assess Prefix Trees, semantic retrieval, and Large Language Models (LLMs) with and without finetuning.
  arXiv  Detail & Related papers  (2024-10-22T21:11:34Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  We propose a pioneering generAtive Cross-modal rEtrieval framework (ACE) for end-to-end cross-modal retrieval. ACE achieves state-of-the-art performance in cross-modal retrieval and outperforms the strong baselines on Recall@1 by 15.27% on average.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries [15.37457156804212]
  Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box. One of the goals of typeahead is to suggest relevant queries to users which are seasonally important. We propose a neural network based natural language processing (NLP) algorithm to incorporate seasonality as a signal.
  arXiv  Detail & Related papers  (2023-08-03T21:14:25Z)
• 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)
• Session-Aware Query Auto-completion using Extreme Multi-label Ranking [61.753713147852125]
  We take the novel approach of modeling session-aware query auto-completion as an e Multi-Xtreme Ranking (XMR) problem. We adapt a popular XMR algorithm for this purpose by proposing several modifications to the key steps in the algorithm. Our approach meets the stringent latency requirements for auto-complete systems while leveraging session information in making suggestions.
  arXiv  Detail & Related papers  (2020-12-09T17:56:22Z)
• Mining Implicit Relevance Feedback from User Behavior for Web Question Answering [92.45607094299181]
  We make the first study to explore the correlation between user behavior and passage relevance. Our approach significantly improves the accuracy of passage ranking without extra human labeled data. In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine.
  arXiv  Detail & Related papers  (2020-06-13T07:02:08Z)
• User Intent Inference for Web Search and Conversational Agents [3.9400263964632836]
  thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain.
  arXiv  Detail & Related papers  (2020-05-28T07:04:42Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)

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.