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Generating Tips from Product Reviews

Published: 08 March 2021 Publication History

Abstract

Product reviews play a key role in e-commerce platforms. Studies show that many users read product reviews before purchase and trust them as much as personal recommendations. However, in many cases, the number of reviews per product is large and finding useful information becomes a challenging task. A few websites have recently added an option to post tips - short, concise, practical, and self-contained pieces of advice about products. These tips are complementary to the reviews and usually add a new non-trivial insight about the product, beyond its title, attributes, and description. Yet, most if not all major e-commerce platforms lack the notion of a tip as a first class citizen and customers typically express their advice through other means, such as reviews. In this work, we propose an extractive method for tip generation from product reviews. We focus on five popular e-commerce domains whose reviews tend to contain useful non-trivial tips that are beneficial for potential customers. We formally define the task of tip extraction in e-commerce by providing the list of tip types, tip timing (before and/or after the purchase), and connection to the surrounding context sentences. To extract the tips, we propose a supervised approach and provide a labeled dataset, annotated by human editors, over 14,000 product reviews using a dedicated tool. To demonstrate the potential of our approach, we compare different tip generation methods and evaluate them both manually and over the labeled set. Our approach demonstrates especially high performance for popular products in the Baby, Home Improvement and Sports & Outdoors domains, with precision of over 95% for the top 3 tips per product.

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  • (2025)Leveraging AI and customer reviews to evaluate technology used by people with disabilitiesDisability and Rehabilitation: Assistive Technology10.1080/17483107.2025.2465603(1-9)Online publication date: 16-Feb-2025
  • (2023)Generating Product Insights from Community Q&AProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615480(4660-4666)Online publication date: 21-Oct-2023
  • (2023)Sentiment-aware Review Summarization with Personalized Multi-task Fine-tuningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615056(2826-2835)Online publication date: 21-Oct-2023
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cover image ACM Conferences
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining
March 2021
1192 pages
ISBN:9781450382977
DOI:10.1145/3437963
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 March 2021

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Author Tags

  1. e-commerce
  2. product reviews
  3. tips generation

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Cited By

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  • (2025)Leveraging AI and customer reviews to evaluate technology used by people with disabilitiesDisability and Rehabilitation: Assistive Technology10.1080/17483107.2025.2465603(1-9)Online publication date: 16-Feb-2025
  • (2023)Generating Product Insights from Community Q&AProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615480(4660-4666)Online publication date: 21-Oct-2023
  • (2023)Sentiment-aware Review Summarization with Personalized Multi-task Fine-tuningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615056(2826-2835)Online publication date: 21-Oct-2023
  • (2023)The Tip of the Buyer: Extracting Product Tips from ReviewsACM Transactions on Internet Technology10.1145/354714023:1(1-30)Online publication date: 23-Feb-2023
  • (2022)Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping WebsitesInformation10.3390/info1303011013:3(110)Online publication date: 25-Feb-2022
  • (2022)Classifier Construction Under Budget ConstraintsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517863(1160-1174)Online publication date: 10-Jun-2022
  • (2022)Personalized Abstractive Opinion TaggingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532037(1066-1076)Online publication date: 6-Jul-2022
  • (2022)Analyzing the Support Level for Tips Extracted from Product ReviewsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531805(2059-2064)Online publication date: 6-Jul-2022
  • (2022)Stylistic Pattern Guided Tip Extraction from Music Reviews2022 4th International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS55630.2022.00077(463-468)Online publication date: Aug-2022

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