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Using Social Data for Personalizing Review Rankings
6th ACM RecSys Workshop on Recommender Systems & the Social Web (2014)
  • Vaishak Suresh, San Jose State University
  • Syeda Roohi, San Jose State University
  • Magdalini Eirinaki, San Jose State University
  • Iraklis Varlamis
Abstract
Almost all users look at online ratings and reviews before buying a product, visiting a business, or using a service. These reviews are independent, authored by other users, and thus may convey useful information to the end user. Reviews usually have an overall rating, but most of the times there are sub-texts in the review body that describe certain features/aspects of the product. The majority of web sites rank these reviews either by date, or by overall “helpfulness”. However, different users look for different qualities in a product/business/service. In this work, we try to address this problem by proposing a system that creates personalized rankings of these reviews, tailored to each individual user. We discuss how social data, ratings, and reviews can be combined to create this personalized experience. We present our work-in-progress using the Yelp Challenge dataset and discuss some first findings regarding implementation and scalability.
Keywords
  • Personalization,
  • recommendation Engine,
  • feature ranking,
  • sentiment analysis
Publication Date
October, 2014
Citation Information
Vaishak Suresh, Syeda Roohi, Magdalini Eirinaki and Iraklis Varlamis. "Using Social Data for Personalizing Review Rankings" 6th ACM RecSys Workshop on Recommender Systems & the Social Web (2014)
Available at: http://works.bepress.com/magdalini_eirinaki/35/