MZK Program for Solving Stiff Systems in Nuclide Kinetics Problems

Mitenkova, E. F.; Solov’eva, E. V.; Manichev, Vladimir; Kozhevnikov, D.

doi:10.1007/s10512-022-00822-2

Cited by 2 publications

(1 citation statement)

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“…The development of the precision marketing theory system requires powerful tools for collecting and analyzing consumer behavior. The collaborative filtering algorithm is just such a tool; so, the two can be deeply combined [22]. Li thinks that there are three common problems in recommendation system: data sparsity, cold start, and scalability, which will directly affect the accuracy of recommendation.…”

Section: Related Workmentioning

confidence: 99%

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

2022

Wireless Communications and Mobile Computing

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e-commerce mode shows great modern commercial value. In particular, online shopping has become a fashion and trend for people because of its convenience and rapidness. How to find the information users that need accurately and quickly in the increasing network information and recommend products is a big problem. Although precision marketing was mainly used in e-commerce activities in the past, due to factors such as the technical basis and data analysis ability at that time, there was not enough technical ability and theoretical basis to deeply mine and make use of the existing data. The collaborative filtering algorithm is one of the most widely used and successful recommendation techniques, but it has obvious defects. In this paper, the nearest neighbor collaborative filtering recommendation algorithm based on statistical eigenvalue classification is proposed in the collaborative filtering algorithm. By calculating the similarity between items, the user’s rating of unrated items is preliminarily predicted, the nearest neighbor of items is formed, and the classified cluster of items is formed. The matrix is filled by the similarity between related items. The cold treatment problem is solved under the optimization of the ant colony algorithm. In the experiment of the model, the optimization rate for the cold start problem is 87.3%.

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Section: Related Workmentioning

confidence: 99%