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SHOPSMART: product recommendations through technical specifications and user reviews
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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents
Napa Valley, California, USA
POSTER SESSION: Poster session 3/information retrieval table of contents
Pages 1501-1502  
Year of Publication: 2008
ISBN:978-1-59593-991-3
Authors
Alexander Yates  Temple University, Philadelphia, PA, USA
James Joseph  Temple University, Philadelphia, PA, USA
Ana-Maria Popescu  Yahoo, Inc., Santa Clara, CA, USA
Alexander D. Cohn  Temple University, Philadelphia, PA, USA
Nick Sillick  Temple University, Philadelphia, PA, USA
Sponsors
ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper describes a new method for providing recommendations tailored to a user's preferences using text mining techniques and online technical specifications of products. We first learn a model that can predict the price of a product given automatically-determined features describing technical specifications and users' opinions. We then use this model to rank a list of products based on individual users' preferences about various features. On a data set collected from Amazon reviews and online technical specifications, rankings produced by this model rank the best product for a user in the 87th percentile of products in its category, on average. Our approach outperforms several comparison systems by 21 percentiles or more.



Collaborative Colleagues:
Alexander Yates: colleagues
James Joseph: colleagues
Ana-Maria Popescu: colleagues
Alexander D. Cohn: colleagues
Nick Sillick: colleagues