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Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model

Journal Article


Abstract


  • Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some "possible friends." In the second stage, with the relationship between image features and users, we build a topic model to further refine the recommendation results. Because some traditional methods, such as variational inference and Gibbs sampling, have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods.

Authors


  •   Huang, Shangrong (external author)
  •   Zhang, Jian (external author)
  •   Schonfeld, D (external author)
  •   Wang, Lei
  •   Hua, Xian-Sheng (external author)

Publication Date


  • 2017

Citation


  • Huang, S., Zhang, J., Schonfeld, D., Wang, L. & Hua, X. (2017). Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model. IEEE Transactions on Multimedia, 19 (6), 1314-1326.

Scopus Eid


  • 2-s2.0-85028327786

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/701

Number Of Pages


  • 12

Start Page


  • 1314

End Page


  • 1326

Volume


  • 19

Issue


  • 6

Place Of Publication


  • United States

Abstract


  • Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some "possible friends." In the second stage, with the relationship between image features and users, we build a topic model to further refine the recommendation results. Because some traditional methods, such as variational inference and Gibbs sampling, have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods.

Authors


  •   Huang, Shangrong (external author)
  •   Zhang, Jian (external author)
  •   Schonfeld, D (external author)
  •   Wang, Lei
  •   Hua, Xian-Sheng (external author)

Publication Date


  • 2017

Citation


  • Huang, S., Zhang, J., Schonfeld, D., Wang, L. & Hua, X. (2017). Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model. IEEE Transactions on Multimedia, 19 (6), 1314-1326.

Scopus Eid


  • 2-s2.0-85028327786

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/701

Number Of Pages


  • 12

Start Page


  • 1314

End Page


  • 1326

Volume


  • 19

Issue


  • 6

Place Of Publication


  • United States