Paper Info
Title
Fuzzy and Crisp Recursive Profiling of Online Reviewers and Businesses
Abstract
Users of online review sites can benefit from knowing the profiles of the businesses as well as the profiles of reviewers who reviewed the businesses. This paper describes crisp and fuzzy meta-clustering techniques to evolve two recursively defined clustering schemes of both businesses and reviewers in parallel, using a real-world dataset supplied by yelp.com. The objective is to profile the businesses and reviewers by grouping them based on similar characteristics. The novelty of the proposed approach is in the fact that the representations of both businesses and reviewers change dynamically throughout the meta-clustering process. A business is represented by static information obtained from the database and dynamic information obtained from the clustering of reviewers who reviewed the business. Similarly, the reviewer representation augments the static representation from the database with profiles of businesses who are reviewed by these reviewers. The resulting web-based service provides a facility for users to find similar businesses/reviewers based on the category of the business, rating, number of reviews and number of check-ins. It also provides a succinct profile of a business or reviewer based on these factors, so users can put the reviews in context. Since an object can belong to multiple clusters in fuzzy meta-clustering, it is possible to absorb some of the extreme groups consisting of outliers in one of the main-stream clusters. As a result the fuzzy meta-clustering leads to more uniformly distributed and moderate profiles.
Year
DOI
Venue
2015
10.1109/TFUZZ.2014.2349532
Fuzzy Systems, IEEE Transactions
Keywords
Field
DocType
databases,clustering algorithms,business,vectors,rough sets
Profiling (computer programming),Computer science,Fuzzy logic,Outlier,Rough set,Artificial intelligence,Novelty,Cluster analysis,Recursion,Machine learning
Journal
Volume
Issue
ISSN
PP
99
1063-6706
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Pawan Lingras11408143.21
Matt Triff201.01