WeRecommend: Recommender System Based on Product Reviews
Author(s):
Vedita Dinesh Velingker , Padre Conceicao College of Engineering; Malony Alphonso, Padre Conceicao College of Engineering
Keywords:
Recommender System, Opinion Mining, Feature, Sentiment, Comparison, Information Extraction
Abstract:
Online reviews are a form of free text that has information about user’s experience and their issues with the product. This information is a rich source for a company’s business intelligence which can be harnessed for the purpose of personalization, product recommendation and better customer understanding. This paper proposes WeRecommend, a recommender system that makes use of consumer opinion expressed online about products, to generate product recommendations. It performs comparisons among a multitude of products, based on opinions provided in reviews by users who have had an experience with the product and recommends much better products. It not only considers the strength of each user’s opinion, but also gives an overall evaluation of each feature for a product. Our recommender system is different from personalized recommender systems that are mainly based on user’s previous browsing history and previously viewed categories. This system focuses on user convictions and recommends to customers products with better subjective user experiences.
Other Details:
| Manuscript Id | : | IJSTEV2I12153
|
| Published in | : | Volume : 2, Issue : 12
|
| Publication Date | : | 01/07/2016
|
| Page(s) | : | 333-337
|
Download Article
