4.7 Article

Personalized recommender system based on friendship strength in social network services

Journal

EXPERT SYSTEMS WITH APPLICATIONS
Volume 69, Issue -, Pages 135-148

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.10.024

Keywords

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

Funding

  1. Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning [2012M3C4A7033346]
  2. National Research Foundation of Korea [2012M3C4A7033346] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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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. (C) 2016 Elsevier Ltd. All rights reserved.

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