Combining Similarity and Sentiment in Opinion Mining for Product Recommendation
Publication Type:
Refereed Original Article
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
This work is supported by Science Foundation Ireland under grant 07/CE/I1147. The Insight
Centre for Data Analytics is supported by Science Foundation Ireland under Grant Number
SFI/12/RC/2289.
R. Dong
CLARITY: Centre for Sensor Web Technologies
School of Computer Science and Informatics
University College Dublin, Dublin, Ireland
E-mail: ruihai.dong@ucd.ie
M. P. O'Mahony
Insight Centre for Data Analytics
School of Computer Science and Informatics
University College Dublin, Dublin, Ireland
E-mail: michael.omahony@ucd.ie
M. Schaal
CLARITY: Centre for Sensor Web Technologies
School of Computer Science and Informatics
University College Dublin, Dublin, Ireland
E-mail: markus.schaal@ucd.ie
K. McCarthy
Insight Centre for Data Analytics
School of Computer Science and Informatics
University College Dublin, Dublin, Ireland
E-mail: kevin.mccarthy@ucd.ie
B. Smyth
Insight Centre for Data Analytics
School of Computer Science and Informatics
University College Dublin, Dublin, Ireland
E-mail: barry.smyth@ucd.ie
2 Ruihai Dong et al.
is achieved by comparing product features or other descriptive elements. The ap-
proach works well when product descriptions are readily available and when they
are detailed enough to aord an eective 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 re-
views, 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 sim-
ilar but superior to a query product according to the opinion of reviewers, and
we demonstrate the practical benets of this approach across a variety of Amazon
product domains.
Digital Object Identifer (DOI):
10.1007/s10844-015-0379-y
Publication Status:
Published
Publication Date:
17/09/2015
Journal:
Journal of Intelligent Information Systems, 2015
Research Group:
Institution:
National University of Ireland, Dublin (UCD)
Open access repository:
Yes
Publication document: