Personalized recommender system based on friendship strength in social network services

Young Duk Seo, Young Gab Kim, Euijong Lee, Doo Kwon Baik

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

The rapid growth of social network services has produced a considerable amount of data, called big social data. Big social data are helpful for improving personalized recommender systems because these enormous data have various characteristics. Therefore, many personalized recommender systems based on big social data have been proposed, in particular models that use people relationship information. However, most existing studies have provided recommendations on special purpose and single-domain SNS that have a set of users with similar tastes, such as MovieLens and Last.fm; nonetheless, they have considered closeness relation. In this paper, we introduce an appropriate measure to calculate the closeness between users in a social circle, namely, the friendship strength. Further, we propose a friendship strength-based personalized recommender system that recommends topics or interests users might have in order to analyze big social data, using Twitter in particular. The proposed measure provides precise recommendations in multi-domain environments that have various topics. We evaluated the proposed system using one month's Twitter data based on various evaluation metrics. Our experimental results show that our personalized recommender system outperforms the baseline systems, and friendship strength is of great importance in personalized recommendation.

Original languageEnglish
Pages (from-to)135-148
Number of pages14
JournalExpert Systems with Applications
Volume69
DOIs
Publication statusPublished - 2017 Mar 1

Keywords

  • Collaborative filtering (CF)
  • Friendship strength
  • Personalized recommender system
  • Social behavior
  • Social network services

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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