Sentiment Analysis and User Similarity for Social Recommender System: An Experimental Study

  • Thi-Ngan Pham
  • Thi-Hong Vuong
  • Thi-Hoai Thai
  • Mai-Vu Tran
  • Quang-Thuy Ha
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)

Abstract

Social recommender system has become an emerging research topic due to the prevalence of online social networking services during the past few years. A social recommender model can be considered the combination of a recommender model and a social information model. Many approaches have been proposed to exploit the social interaction or connections among users to overcome the defect of traditional recommender systems assuming that all the users are independent and identically distributed. In this paper, we propose a social recommender system using memory based collaborative filtering models with user-oriented methods as basic models, in which we conduct an analysis on the correlations between social relations and user interest similarities. We also combine techniques of sentiment analysis to get dataset of users with their favorite products; this dataset is the input for the social recommender system. Our experiments on giving recommendations for Facebook users about mobile phones show the efficiency of the proposed approach.

Keywords

Social recommender system Collaborative filtering Opinion mining/sentiment analysis User similarity 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Thi-Ngan Pham
    • 1
    • 2
  • Thi-Hong Vuong
    • 1
  • Thi-Hoai Thai
    • 1
  • Mai-Vu Tran
    • 1
  • Quang-Thuy Ha
    • 1
  1. 1.College of Technology (UET)Vietnam National University, Hanoi (VNU)HanoiVietnam
  2. 2.The Vietnamese People’s Police AcademyHanoiVietnam

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