Journal of Applied Mathematics
Volume 2013 (2013), Article ID 248084, 8 pages
http://dx.doi.org/10.1155/2013/248084
Research Article

Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

Jinyu Hu,1 Zhiwei Gao,2 and Weisen Pan1

1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
2Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

Received 14 June 2013; Accepted 22 June 2013

Academic Editor: Dexing Kong

Copyright © 2013 Jinyu Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.