# Product Question Answering in E-Commerce: A Survey

arXiv: https://arxiv.org/abs/2302.08092 · HTML: https://mlsanity.com/paper/2302.08092/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 76 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 30 of 38)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [AmazonQA: A Review-Based Question Answering Task](https://mlsanity.com/paper/1908.04364/) (2019)
- [Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge](https://mlsanity.com/paper/2104.06828/) (2021)
- [Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product](https://mlsanity.com/paper/2009.07162/) (2020)
- [OpinionDigest: A Simple Framework for Opinion Summarization](https://mlsanity.com/paper/2005.01901/) (2020)
- [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019)
- [Addressing Complex and Subjective Product-Related Queries with Customer Reviews](https://mlsanity.com/paper/1512.06863/) (2015)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience](https://mlsanity.com/paper/1801.05032/) (2018)
- [SubjQA: A Dataset for Subjectivity and Review Comprehension](https://mlsanity.com/paper/2004.14283/) (2020)
- [Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework](https://mlsanity.com/paper/2109.05897/) (2021)
- [Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce](https://mlsanity.com/paper/1711.08726/) (2017)
- [Meaningful Answer Generation of E-Commerce Question-Answering](https://mlsanity.com/paper/2011.07307/) (2020)
- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems](https://mlsanity.com/paper/1610.08095/) (2016)
- [Persona-Aware Tips Generation](https://mlsanity.com/paper/1903.02156/) (2019)
- [Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce](https://mlsanity.com/paper/2008.11972/) (2020)
- [Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products](https://mlsanity.com/paper/2105.08956/) (2021)
- [Review-Driven Answer Generation for Product-Related Questions in E-Commerce](https://mlsanity.com/paper/1905.01994/) (2019)
- [Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling](https://mlsanity.com/paper/2006.15599/) (2020)
- [Diverse and Specific Clarification Question Generation with Keywords](https://mlsanity.com/paper/2104.10317/) (2021)

## Built on by (most cited first, 10 of 10)

- [eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data](https://mlsanity.com/paper/2402.08831/) (2024)
- [AGILE: A Novel Reinforcement Learning Framework of LLM Agents](https://mlsanity.com/paper/2405.14751/) (2024)
- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [Question Suggestion for Conversational Shopping Assistants Using Product Metadata](https://mlsanity.com/paper/2405.01738/) (2024)
- [An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification](https://mlsanity.com/paper/2407.01137/) (2024)
- [BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain](https://mlsanity.com/paper/2409.20075/) (2024)
- [Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge](https://mlsanity.com/paper/2309.05938/) (2023)
- [Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation](https://mlsanity.com/paper/2401.09785/) (2024)
- [Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering](https://mlsanity.com/paper/2409.16025/) (2024)
- [QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering](https://mlsanity.com/paper/2506.04020/) (2025)

## Abstract (arXiv)

Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing datasets and evaluation protocols for each setting. We further summarize the most significant challenges that characterize PQA from general QA applications and discuss their corresponding solutions. Finally, we conclude this paper by providing the prospect on several future directions.