A New Efficient Algorithm for Weighted Vertex Cover in Bipartite Graphs Based on a Dual Problem

Zhang, Yujiao; Duan, Xiao Min; Yue, Xuerong; Chen, Zhibin

doi:10.1109/itme.2018.00016

Cited by 1 publication

(2 citation statements)

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“…The material diffusion method behaves similarly to the random walk process' resource allocation mechanism [14] . Based on the user-commodity bipartite graph, it is assumed that each good has a certain amount of some recommending power, i.e., the goods selected by a user have some abstract ability to recommend other goods to that user, and the goods take resources and pass more resources to their more preferred goods.…”

Section: Mass Diffusion Recommendation Algorithmmentioning

confidence: 99%

“…The recommendation algorithm is the heart and soul of the recommendation system, and a high-quality recommendation algorithm will have an impact on the overall performance and quality of the recommendations. Currently, the most common recommendation algorithms are content-based recommendation algorithms [12] , collaborative filtering recommendation algorithms [13] , and bipartite graph based recommendation algorithms [14] , etc. A recommendation system provides a user with a product that the user could be interested in while also determining a user-commodity connection based on the user's historical behavior.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

Sun,

Shi,

Liu

et al. 2024

Big Data Min. Anal.

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Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less accurate recommendation algorithm. The aim of this paper is to propose improvements on the standard substance diffusion algorithm, taking into account the influence of the user's rating on the recommended item, adding a moderating factor, and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm. An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results. Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

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Section: Mass Diffusion Recommendation Algorithmmentioning

confidence: 99%