Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation | Zendy

Jinyu Hu | Zendy; Zhiwei Gao | Zendy; Weisen Pan | Zendy

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Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

Author(s) -

Jinyu Hu,

Zhiwei Gao,

Weisen Pan

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/248084

Subject(s) - computer science , similarity (geometry) , point (geometry) , precision and recall , resource (disambiguation) , algorithm , simple (philosophy) , data mining , artificial intelligence , mathematics , image (mathematics) , computer network , geometry , philosophy , epistemology

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

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