Incorporating tie strength in robust social recommendation

Youliang Zhong, Jian Yang, Robertus Nugroho

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-The-Art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015
EditorsCarminati Barbara, Latifur Khan
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages63-70
Number of pages8
ISBN (Electronic)9781467372787, 9781467372770
ISBN (Print)9781467372794
DOIs
Publication statusPublished - 17 Aug 2015
Event4th IEEE International Congress on Big Data, BigData Congress 2015 - New York City, United States
Duration: 27 Jun 20152 Jul 2015

Other

Other4th IEEE International Congress on Big Data, BigData Congress 2015
CountryUnited States
CityNew York City
Period27/06/152/07/15

Keywords

  • Recommender Systems
  • Social Media
  • Social Recommendation
  • Robust Matrix Factorization
  • Tie Strength

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