# Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

arXiv: https://arxiv.org/abs/2105.08956 · HTML: https://mlsanity.com/paper/2105.08956/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [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)
- [Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce](https://mlsanity.com/paper/1711.08726/) (2017)
- [Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems](https://mlsanity.com/paper/1610.08095/) (2016)
- [Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce](https://mlsanity.com/paper/2008.11972/) (2020)
- [Supervised Transfer Learning for Product Information Question Answering](https://mlsanity.com/paper/1901.02539/) (2019)
- [Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation](https://mlsanity.com/paper/1910.08270/) (2019)

## Built on by (most cited first, 12 of 12)

- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [InstructPTS: Instruction-Tuning LLMs for Product Title Summarization](https://mlsanity.com/paper/2310.16361/) (2023)
- [Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://mlsanity.com/paper/2306.03411/) (2023)
- [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024)
- [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024)
- [Controllable Decontextualization of Yes/No Question and Answers into Factual Statements](https://mlsanity.com/paper/2401.09775/) (2024)
- [Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction](https://mlsanity.com/paper/2604.26209/) (2026)
- [Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data](https://mlsanity.com/paper/2302.12139/) (2023)
- [OpinionConv: Conversational Product Search with Grounded Opinions](https://mlsanity.com/paper/2308.04226/) (2023)
- [Evaluation Metrics of Language Generation Models for Synthetic Traffic Generation Tasks](https://mlsanity.com/paper/2311.12534/) (2023)
- [Identifying Shopping Intent in Product QA for Proactive Recommendations](https://mlsanity.com/paper/2404.06017/) (2024)
- [Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering](https://mlsanity.com/paper/2409.16025/) (2024)

## Abstract (arXiv)

Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer-generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.