A Novel Review Helpfulness Measure Based on the User-Review-Item Paradigm
Abstract
Review platforms are viral online services where users share and read opinions about products (e.g., a smartphone) or experiences (e.g., a meal at a restaurant). Other users may be influenced by such opinions when deciding what to buy. The usability of review platforms is currently limited by the massive number of opinions on many products. Therefore, showing only the most helpful reviews for each product is in the best interest of both users and the platform (e.g., Amazon). The current state of the art is far from accurate in predicting how helpful a review is. First, most existing works lack compelling comparisons as many studies are conducted on datasets that are not publicly available. As a consequence, new studies are not always built on top of prior baselines. Second, most existing research focuses only on features derived from the review text, ignoring other fundamental aspects of the review platforms (e.g., the other reviews of a product, the order in which they were submitted).
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 11, 2023
- Source ID
- 10.1145/3585280
Entities
People
- Dongkai Chen
- Luca Pajola
- Mauro Conti
- V. S. Subrahmanian
Organizations
- Dartmouth College
- Northwestern University
- Office of Naval Research
- University of Padua