Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

• Main Authors: Jinyu Hu, Zhiwei Gao, Weisen Pan
• Format: Article
• Language: English
• Published: Hindawi Limited 2013-01-01
• Series: Journal of Applied Mathematics
• Online Access: http://dx.doi.org/10.1155/2013/248084

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.