UIS-LDA : a user recommendation based on social connections and interests of users in uni-directional social networks

Ke XU, Yi CAI, Huaqing MIN, Xushen ZHENG, Haoran XIE, Tak Lam WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

7 Citations (Scopus)


The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worthwhile to consider both factors at the same time for better recommendations. Previous recommender works on implicit follow or not feedbacks become unqualified, mainly because of the coarse users' preferences inferring, which cannot distinguish whether one follows the other is based on her social connection or individual interest. In this paper, we present a new user recommendation method which is capable of recommending candidate followees who have similar interest and closer social connection relevant to a target user. As its core, a novel topic model namely UIS-LDA is designed to jointly model a user's preferences with respect to the set of latent interest topics and social topics. The experiments using Twitter dataset proves that our proposed method effective in improving the Precision, Conversion Rate F1 score and NDCG.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Web Intelligence
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Print)9781450349512
Publication statusPublished - Aug 2017
Externally publishedYes
EventIEEE/WIC/ACM International Conference on Web Intelligence 2017 - Leipzig, Germany
Duration: 23 Aug 201726 Aug 2017

Publication series

NameProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017


ConferenceIEEE/WIC/ACM International Conference on Web Intelligence 2017
Abbreviated titleWI 2017
Internet address


  • Topic modeling
  • Uni-directional social networks
  • User recommendation


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