Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search

• URL: http://arxiv.org/abs/2507.22213v1
• Date: Tue, 29 Jul 2025 20:20:07 GMT
• Title: Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search
• Authors: Jayanth Yetukuri, Ishita Khan,
• Abstract summary: This work introduces a robust data pipeline designed to mine and analyze large-scale buyer query logs.<n>The pipeline systematically captures patterns indicative of latent purchase intent, enabling the construction of a high-fidelity, intent-rich dataset.<n>Our findings highlight the value of intent-centric modeling in bridging the gap between sparse user inputs and complex product discovery goals.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and modeling buyer intent is a foundational challenge in optimizing search query reformulation within the dynamic landscape of e-commerce search systems. This work introduces a robust data pipeline designed to mine and analyze large-scale buyer query logs, with a focus on extracting fine-grained intent signals from both explicit interactions and implicit behavioral cues. Leveraging advanced sequence mining techniques and supervised learning models, the pipeline systematically captures patterns indicative of latent purchase intent, enabling the construction of a high-fidelity, intent-rich dataset. The proposed framework facilitates the development of adaptive query rewrite strategies by grounding reformulations in inferred user intent rather than surface-level lexical signals. This alignment between query rewriting and underlying user objectives enhances both retrieval relevance and downstream engagement metrics. Empirical evaluations across multiple product verticals demonstrate measurable gains in precision-oriented relevance metrics, underscoring the efficacy of intent-aware reformulation. Our findings highlight the value of intent-centric modeling in bridging the gap between sparse user inputs and complex product discovery goals, and establish a scalable foundation for future research in user-aligned neural retrieval and ranking systems.

Related papers

• Preference Learning for AI Alignment: a Causal Perspective [55.2480439325792]
  We frame this problem in a causal paradigm, providing the rich toolbox of causality to identify persistent challenges.<n>Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation.<n>We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness.
  arXiv  Detail & Related papers  (2025-06-06T10:45:42Z)
• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [55.914891182214475]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• From Intent Discovery to Recognition with Topic Modeling and Synthetic Data [0.0]
  Customer utterances are characterized by infrequent word co-occurences and high term variability.<n>We propose an agentic LLM framework for topic modeling and synthetic query generation.<n>We show that LLM-generated intent descriptions and keywords can effectively substitute for human-curated versions.
  arXiv  Detail & Related papers  (2025-05-16T12:20:31Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models generate target documents directly from a query.<n>We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Behavior Modeling Space Reconstruction for E-Commerce Search [32.46624795081706]
  Search systems model user behaviors by combining user preference and query item relevance statically, often through a fixed logical 'and' relationship.<n>This paper reexamines existing approaches through a unified lens using both causal graphs and Venn diagrams.<n>To surmount these challenges, our research introduces a novel framework, which enhances search accuracy through two components to reconstruct the behavior modeling space.
  arXiv  Detail & Related papers  (2025-01-30T09:17:04Z)
• Can foundation models actively gather information in interactive environments to test hypotheses? [56.651636971591536]
  We introduce a framework in which a model must determine the factors influencing a hidden reward function.<n>We investigate whether approaches such as self- throughput and increased inference time improve information gathering efficiency.
  arXiv  Detail & Related papers  (2024-12-09T12:27:21Z)
• Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders [4.3012765978447565]
  We introduce a framework that combines neural contextual matrix factorization with autoencoders to predict user ratings for items.<n>We conduct experiments on various real-world datasets and compare the results against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-11-30T18:24:42Z)
• CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data.<n>Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates.<n>We propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning [25.356999988217325]
  Relevance modeling aims to locate desirable items for corresponding queries. auxiliary query-item interactions extracted from user historical behavior data could provide hints to reveal users' search intents further. Our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views.
  arXiv  Detail & Related papers  (2023-08-10T06:52:53Z)
• Unified Embedding Based Personalized Retrieval in Etsy Search [0.206242362470764]
  We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end. Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
  arXiv  Detail & Related papers  (2023-06-07T23:24:50Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)

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.