On top-k recommendation using social networks

Xiwang Yang, Harald Steck, Yang Guo, Yong Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recommendation accuracy can be improved by incorporating trust relationships derived from social networks. Most recent work on social network based recommendation is focused on minimizing the root mean square error (RMSE). Social network based top-k recommendation, which recommends to a user a small number of items at a time, is not well studied. In this paper, we conduct a comprehensive study on improving the accuracy of top-k recommendation using social networks. We first show that the existing social-trust enhanced Matrix Factorization (MF) models can be tailored for top-k recommendation by including observed and missing ratings in their training objective functions. We also propose a Nearest Neighbor (NN) based top-k recommendation method that combines users' neighborhoods in the trust network with their neighborhoods in the latent feature space. Experimental results on two publicly available datasets show that social networks can significantly improve the top-k hit ratio, especially for cold start users. Surprisingly, we also found that the technical approach for combining feedback data (e.g. ratings) with social network information that works best for minimizing RMSE works poorly for maximizing the hit ratio, and vice versa.

Original languageEnglish (US)
Title of host publicationRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
Number of pages8
StatePublished - 2012
Event6th ACM Conference on Recommender Systems, RecSys 2012 - Dublin, Ireland
Duration: Sep 9 2012Sep 13 2012

Publication series

NameRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems


Other6th ACM Conference on Recommender Systems, RecSys 2012


  • Matrix factorization
  • Recommender system
  • Social network

ASJC Scopus subject areas

  • Software


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