Options
Combining similarity and sentiment in opinion mining for product recommendation
Date Issued
2016-04
Date Available
2016-09-17T01:00:13Z
Abstract
In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Springer
Journal
Journal of Intelligent Information Systems
Volume
46
Issue
2
Start Page
1
285
End Page
28
312
Copyright (Published Version)
2015 Springer Science+Business Media New York
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
53
Acquisition Date
Mar 28, 2024
Mar 28, 2024
Views
4916
Last Week
1
1
Last Month
3
3
Acquisition Date
Mar 28, 2024
Mar 28, 2024
Downloads
2137
Last Week
3
3
Last Month
16
16
Acquisition Date
Mar 28, 2024
Mar 28, 2024