Abstract
This paper uses a novel recursive meta-profiling technique where profiles from one set of objects dynamically change the representation of another set of objects. Two profiling schemes evolve in parallel influencing each other through indirect recursion. This is demonstrated with the help of a yelp.com dataset consisting of businesses and reviewers. A business is represented by static information obtained from the database and dynamic information obtained from 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 service provides a facility for users to find similar businesses/reviewers based on the grading of the business, easy/hard grading, and types of businesses. It also provides a succinct profile of business/reviewer based on these factors, so users can put the reviews in context.
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Triff, M., Lingras, P. (2013). Recursive Profiles of Businesses and Reviewers on Yelp.com. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_35
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DOI: https://doi.org/10.1007/978-3-642-41218-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41217-2
Online ISBN: 978-3-642-41218-9
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