The Research of Recommendation Algorithm based on Complete Tripartite Graph Model

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

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The Crossing Numbers of Join Products of Paths and Cycles with Four Graphs of Order Five

Mathematics ◽

10.3390/math9111277 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1277

Author(s):

Michal Staš

Keyword(s):

Connected Graph ◽

Crossing Numbers ◽

Tripartite Graph ◽

Complete Tripartite Graph

The main aim of the paper is to establish the crossing numbers of the join products of the paths and the cycles on n vertices with a connected graph on five vertices isomorphic to the graph K1,1,3\e obtained by removing one edge e incident with some vertex of order two from the complete tripartite graph K1,1,3. The proofs are done with the help of well-known exact values for the crossing numbers of the join products of subgraphs of the considered graph with paths and cycles. Finally, by adding some edges to the graph under consideration, we obtain the crossing numbers of the join products of other graphs with the paths and the cycles on n vertices.

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On the genus of the complete tripartite graph Kn,n,1

Discrete Mathematics ◽

10.1016/j.disc.2016.09.017 ◽

2017 ◽

Vol 340 (3) ◽

pp. 508-515 ◽

Cited By ~ 1

Author(s):

Valentas Kurauskas

Keyword(s):

Tripartite Graph ◽

Complete Tripartite Graph

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A Tripartite Graph Recommendation Algorithm Based on Item Information and User Preference

2019 6th International Conference on Systems and Informatics (ICSAI) ◽

10.1109/icsai48974.2019.9010319 ◽

2019 ◽

Author(s):

Zhenyu Yao ◽

Jinkuan Wang ◽

Yinghua Han

Keyword(s):

User Preference ◽

Item Information ◽

Recommendation Algorithm ◽

Tripartite Graph

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A Video Recommendation Algorithm Based on Hyperlink-Graph Model

International Journal of Software Innovation ◽

10.4018/ijsi.2017070104 ◽

2017 ◽

Vol 5 (3) ◽

pp. 49-63

Author(s):

Songtao Shang ◽

Wenqian Shang ◽

Minyong Shi ◽

Shuchao Feng ◽

Zhiguo Hong

Keyword(s):

Graph Model ◽

User Interest ◽

Recommendation Algorithm ◽

Internet Users ◽

Recommendation Algorithms ◽

The Matrix ◽

Personal Recommendation ◽

Low Efficiency ◽

Sports Videos ◽

Video Recommendation

The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

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