Collaborative Filtering-Based Recommendation of Online Social Voting

Xiwang Yang, Chao Liang, Miao Zhao, Hongwei Wang, Hao Ding, Yong Liu, Yang Li, Junlin Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users' interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.

Original languageEnglish (US)
Article number7866820
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Computational Social Systems
Volume4
Issue number1
DOIs
StatePublished - Mar 2017

Keywords

  • Collaborative filtering
  • online social networks (OSNs)
  • recommender systems (RSs)
  • social voting

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

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