A hybrid algorithm for recommendation Twitter peers

James N.K. Liu, Zongnong Meng, Yanxing Hu, Yulin He, Chi Keung Simon Shiu, Wing Sing Cho

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper presents a hybrid algorithm in the area of peer recommendation in Twitter. Due to the big data issue on Twitter, we define a filtering strategy to reduce the number of candidates who might be recommended to the target user. Meanwhile, we refine the content-based similarity and graph-based similarity algorithms proposed by other academics. Moreover, we define a user model and weighting formula to leverage these two algorithms. According to the similarity degree between the candidates and the target user, we recommend the top k most similar candidates to the target user as our focused peers. In order to evaluate the effectiveness of our proposed algorithms and other algorithms, we conduct a personalized survey and employ measurements like recall, precision and F1 metric. The evaluation results demonstrate that our hybrid algorithm is better than the pure content-based similarity algorithm and pure graph-based similarity algorithm.
Original languageEnglish
Title of host publicationProceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014
PublisherAssociation for Computing Machinery
Pages644-649
Number of pages6
ISBN (Print)9781450324694
DOIs
Publication statusPublished - 1 Jan 2014
Event29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of
Duration: 24 Mar 201428 Mar 2014

Conference

Conference29th Annual ACM Symposium on Applied Computing, SAC 2014
CountryKorea, Republic of
CityGyeongju
Period24/03/1428/03/14

Keywords

  • Peer recommendation
  • Ranking strategy
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Software

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