Review Recommendation for Points of Interest's Owners
Thiago R. P. Prado, Mirella Moura Moro · 2017
Websites that provide reviews for services and products deal with big volumes of data (many users writing many reviews for many items). Then, recommendation algorithms come to the rescue in matching reviews to the consumers who are reading them. Such online review applications usually recommend the most useful reviews for consumers to read. In this work, we propose a new perspective to this problem: how to evaluate the helpfulness of a review from the business owner's perspective. Our solution uses the review's aspects and sentiments, and ranks the most helpful ones seeking to assist establishment owners improve their businesses. Our experimental evaluations consider experts opinion and show that our solution is very close to the ideal ranking.