SHOPSMART: product recommendations through technical specifications and user reviews

Authors:
Alexander Yates;James Joseph;Ana-Maria Popescu;Alexander D. Cohn;Nick Sillick
Affiliations:
Temple University, Philadelphia, PA, USA;Temple University, Philadelphia, PA, USA;Yahoo, Inc., Santa Clara, CA, USA;Temple University, Philadelphia, PA, USA;Temple University, Philadelphia, PA, USA
Venue:
Proceedings of the 17th ACM conference on Information and knowledge management
Year:
2008

Citing 3
Cited 0

Fab: content-based, collaborative recommendation

Communications of the ACM
Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
Opinion observer: analyzing and comparing opinions on the Web

WWW '05 Proceedings of the 14th international conference on World Wide Web

Quantified Score

Hi-index	0.00

Visualization

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.