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)


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
Number of pages6
ISBN (Print)9781450324694
Publication statusPublished - 1 Jan 2014
Event29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of
Duration: 24 Mar 201428 Mar 2014


Conference29th Annual ACM Symposium on Applied Computing, SAC 2014
Country/TerritoryKorea, Republic of


  • Peer recommendation
  • Ranking strategy
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Software


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