Papers with e-commerce platforms
Neural Network based Extreme Classification and Similarity Models for Product Matching (N18-3)
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| Challenge: | Matching a seller listed item to an appropriate product has become a fundamental step for e-commerce platforms. |
| Approach: | They propose to use a shallow neural network to match a seller's item to an appropriate product . they also propose a similarity approach based on deep siamese network to train and infer product information. |
| Outcome: | The proposed models outperform the baseline models by more than 5% in terms of accuracy and are capable of efficient training and inference. |
Centrality-aware Product Retrieval and Ranking (2024.emnlp-industry)
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| Challenge: | Ambiguity and complexity of user queries often lead to mismatch between user’s intent and retrieved product titles or documents. |
| Approach: | They propose a user-intent centrality optimization approach which optimizes for the user intent in semantic product search. |
| Outcome: | The proposed approach improves product ranking efficiency for ambiguous queries and lexical terms with alphanumeric characters. |
Graph-based Multilingual Product Retrieval in E-Commerce Search (2021.naacl-industry)
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| Challenge: | Modern e-commerce search systems require product retrieval under multilingual scenarios. |
| Approach: | They propose a universal multilingual retrieval system that captures interactions between search queries and items in e-commerce search. |
| Outcome: | The proposed system outperforms state-of-the-art retrieval models on five countries and has been deployed in production for multiple countries. |
Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)
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| Challenge: | Recent scene marketing has proved effective for offline shopping. |
| Approach: | They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. |
| Outcome: | The proposed system can be automated and tested on a real-world e-commerce recommendation platform. |
Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization (2023.acl-industry)
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| Challenge: | e-commerce platforms are encountering increasingly complex product categorization scenarios . multiple business domains correspond to different category taxonomies, with different depths and distinct literal expressions of category names. |
| Approach: | They propose a taxonomy-agnostic framework that calculates semantic relatedness between product titles and category names in the vector space. |
| Outcome: | The proposed framework outperforms strong baselineson three dynamic multi-domain product categorization tasks. |
Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints (2022.emnlp-industry)
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| Challenge: | We have deployed reliable and precise large-scale machine translation systems for several Indian regional languages. |
| Approach: | They develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. |
| Outcome: | The proposed model improves over iterations for English to Hindi and for other languages. |
TrendPulse: A Simple yet Efficient Framework for Capturing Viral E-Commerce Spikes via LLM-Driven Contextualization (2026.acl-industry)
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| Challenge: | Modern e-commerce platforms mostly depend on reactive discovery, where products surface only after users search for them. |
| Approach: | They propose a framework that identifies regional search momentum and leverages Large Language Model to transform spikes into semantic trends. |
| Outcome: | The proposed framework shows consistent improvements across multiple business metrics and overall user experience. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)
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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng
| Challenge: | Structured product information is a major bottleneck for the efficiency of e-commerce platforms. |
| Approach: | They propose a data-driven approach to generate product structured representations using product metadata. |
| Outcome: | Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms. |
AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment (2025.emnlp-industry)
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| Challenge: | Existing methods for assessing review quality are unscalable across domains and fail to adapt to evolving content patterns. |
| Approach: | They propose an LLM-based agent framework that automates the discovery of interpretable features. |
| Outcome: | The proposed framework improves on a large-scale online platform with a billion-level user base. |
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. |
AttributeForge: An Agentic LLM Framework for Automated Product Schema Modeling (2025.emnlp-industry)
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Yunhan Huang, Klevis Ramo, Andrea Iovine, Melvin Monteiro, Sedat Gokalp, Arjun Bakshi, Hasan Turalic, Arsh Kumar, Jona Neumeier, Ripley Yates, Rejaul Monir, Simon Hartmann, Tushar Manglik, Mohamed Yakout
| Challenge: | e-commerce platforms are producing only tens of attributes per month for schema modeling . authors present a framework to automate end-to-end product schema modeling using Large Language Models . |
| Approach: | They introduce a framework to automate end-to-end product schema modeling using Large Language Models. |
| Outcome: | The proposed framework achieves an 88 increase in modeling throughput while delivering superior quality. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
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| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
Generating Attractive and Authentic Copywriting from Customer Reviews (2024.naacl-long)
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| Challenge: | Typical approaches to copywriting focus on product attributes, leading to dull and repetitive content. |
| Approach: | They propose to generate copywriting based on customer reviews as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes. |
| Outcome: | The proposed framework outperforms baseline and zero-shot large language models in terms of both attractiveness and faithfulness. |
Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce (2025.findings-emnlp)
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Arnav Attri, Anuj Attri, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing research has not explored the joint task of emotion detection and explanatory span identification in e-commerce reviews. |
| Approach: | They propose a joint task unifying Emotion detection and Opinion Trigger extraction (EOT) which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories). |
| Outcome: | The proposed framework surpasses zero-shot and chain-of-thought techniques across e-commerce domains. |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| Challenge: | Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing . |
| Approach: | They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce. |
| Outcome: | The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks. |
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)
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| Challenge: | Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness. |
| Approach: | They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed. |
| Outcome: | The proposed method can lead to performance increase of 8.5% compared to the best performing text-only model. |
Similarity = Value? Consultation Value-Assessment and Alignment for Personalized Search (2025.emnlp-main)
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| Challenge: | Existing methods rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but this lacks exploration of needs in user consultations. |
| Approach: | They propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. |
| Outcome: | The proposed model outperforms baselines on public and commercial datasets on both retrieval and ranking tasks. |
Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering (2024.emnlp-main)
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| Challenge: | Product-related question answering (PQA) involves utilizing product-related resources to provide precise answers to users. |
| Approach: | They propose a task of multilingual cross-market product-based question answering that combines product-related questions with product-specific questions from a multilingual marketplace. |
| Outcome: | The proposed task provides answers to product-related questions in a multilingual marketplace even in fewer languages. |
QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering (2025.acl-long)
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| Challenge: | Existing review-based product question answering systems generate only a single answer, ignoring the diversity of viewpoints. |
| Approach: | They propose a task which aims to summarize diverse customer opinions into representative Key Points and quantify their prevalence to effectively answer user queries. |
| Outcome: | The proposed task summarizes diverse customer opinions into representative Key Points and quantifies their prevalence to answer user queries. |
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. |