Papers with product search
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (2022.findings-emnlp)
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| Challenge: | Existing models rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. |
| Approach: | They propose a task where a model is required to learn whether a triple is salient . they propose supervised salience evaluation using a new Benchmark dataset . |
| Outcome: | The proposed task is based on a new Benchmark dataset of salience evaluation in e-commerce . it shows that saliency evaluation is hard, where models perform poorly on evaluation set . |
KD-Boost: Boosting Real-Time Semantic Matching in E-commerce with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Existing SOTA techniques for semantic matching are mostly based on Siamese networks. |
| Approach: | They propose a novel knowledge distillation algorithm designed for real-time semantic matching . they train low latency accurate student models by leveraging soft labels from a teacher model . |
| Outcome: | The proposed algorithm outperforms teacher and SOTA knowledge distillation benchmarks on e-commerce datasets. |
Small Agents, Big Gains: Journey-Aware and Critic-Guided Simulation for Long-Horizon Shopping Dialogues (2026.acl-industry)
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| Challenge: | e-commerce assistants must support inspiration, comparison, and tool-grounded fact-checking . lack of data-coverage and verification problem hampers efficient, deployable models . eaa: "training trajectories must cover diverse user workflows with high fidelity" |
| Approach: | They propose a system that synthesizes diverse, faithful, and policy-aligned shopping trajectories . a small model can significantly outperform same-size baselines and surpass a large-model baseline . |
| Outcome: | The proposed model outperforms existing models and surpasses large models with 8 higher inference throughput. |
Identifying High Consideration E-Commerce Search Queries (2024.emnlp-industry)
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| Challenge: | Identifying high consideration queries is essential for e-commerce sites to better serve user needs . ecommerce sites can create or serve customized content for specific queries . |
| Approach: | They propose an engagement-based Query Ranking approach to identify potential engagement levels with query-related shopping knowledge content during product search. |
| Outcome: | The proposed method outperforms human-selected queries in terms of customer impact . human evaluation shows a precision of 96% for HC queries identified by the model . |
Learning to Rewrite Negation Queries in Product Search (2025.coling-industry)
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| Challenge: | Negations in product search are often used to articulate unwanted product features or components. |
| Approach: | They propose a query rewriting approach to enhance product search performance . they use large language models to extract query reawrites from product text . their results pave the way for further research on enhancing search performance of queries with negations . |
| Outcome: | The proposed approach improves search performance by 3.17% for queries with negations. |
ShopperBench: A Benchmark for Personalized Shopping with Persona-Guided Simulation (2026.eacl-industry)
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| Challenge: | Existing evaluation frameworks lack mechanisms to assess Personalized shopping agents' ability to adapt their strategies to heterogeneous user preferences and decisionmaking patterns. |
| Approach: | They propose a persona-guided benchmark that augments shopping trajectories with personas . they propose persona Fidelity, Persona-Query Alignment, and Path Consistency . |
| Outcome: | The proposed benchmark captures how shopper types navigate product search and selection . it measures persona Fidelity, Persona-Query Alignment, and Path Consistency . |
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search (2023.acl-industry)
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| Challenge: | Frequently Asked Question (FAQ) retrieval aims at retrieving question-answer pairs for a given user query. |
| Approach: | They propose to use an intent classifier to predict whether a query is looking for an FAQ and a reformulation model to rewrite the query into a natural question to improve retrieval performance. |
| Outcome: | The proposed method improves 12% on Hit@1 on retrieving ground-truth FAQs while reducing latency by 95% compared to baseline systems. |
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
| Approach: | They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios. |
| Outcome: | The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation. |
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)
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Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability. |
| Approach: | They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain. |
| Outcome: | The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset. |
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)
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Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu
| Challenge: | Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data. |
| Approach: | They propose a prompt tuning approach to extract attributes from product information using mixed prompts. |
| Outcome: | The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting. |
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data (2023.findings-acl)
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| Challenge: | Structure Aware Dense Retrieval (SANTA) model encodes user queries and structured data in one universal embedding space for retrieving structured data. |
| Approach: | They propose to use structured data and unstructured data to encode queries and structured data in one universal embedding space for retrieving structured data. |
| Outcome: | The proposed model achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting. |
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)
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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate . |
| Approach: | They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity . |
| Outcome: | The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds. |