A collaborative filtering based online teaching recommendation system for higher vocational colleges is proposed. UserCF system for user oriented personalized recommendation and object-oriented personalized recommenda...
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New media hot events are currently in a complex network environment. Today's mass emergencies are hot events that spread quickly and gather many people. Based on this research background, the paper proposes to use...
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New media hot events are currently in a complex network environment. Today's mass emergencies are hot events that spread quickly and gather many people. Based on this research background, the paper proposes to use the nonlinear differential equation method to simulate the propagation of mass emergencies. We strive to achieve the goal of minimizing the total social loss through economic subsidies, taking into account the government's use of police force and the degree of social legality. At the same time, we construct a nonlinear system differential model based on the semi-Markov switching space control process. Research shows that the algorithm does not rely on system parameter information. At the same time, the new media hot event push algorithm has good adaptability to the environment.
This article is dedicated to discussing the design and improvement of precision marketing push algorithm under the deep learning target detection system. Scientific progress has made the network form a huge database, ...
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This article is dedicated to discussing the design and improvement of precision marketing push algorithm under the deep learning target detection system. Scientific progress has made the network form a huge database, and each software platform can accurately locate the user's market by analyzing the data, determine their interests, and carry out marketing pushes. Based on deep learning algorithms and models, simulation training can be performed on user rating data. It clusters users according to their preferences and performs data filtering to control the impact of data sparseness or differences between users. It uses the relevant similarity of users and uses neighborhood collaborative filtering to make accurate judgments and push. This article is based on the SVD++ (Singular Value Decomposition, SVD++) algorithm and optimized to achieve higher push accuracy. The previous explained the SDD (Single Shot MultiBox Detector, SDD) algorithm, Pearson correlation similarity, neighborhood-based collaborative filtering model, neural network model, and Rayleigh channel system to explain the application of the deep learning target detection system in precision marketing, then verify the feasibility of the improved SVD++ algorithm through experiments. In the experiment, through the comparative analysis of UserCF (Collaborative Filtering, UserCF), Slope one, SVD++, OrdRec, Pure, AllRank, JSVD++, MSSVD++ (Medical Society for the Study of Venereal Diseases, MSSVD++), NSSVD++ (Neighborhood Sampling Singular Value Decomposition) algorithms, the test focuses on JSVD++, MSSVD++, NSSVD++ algorithms. And it is concluded that the NSSVD++ algorithm, that is, the neighborhood sampling method, is the most effective and has the best marketing recommendation effect. Among them, the 1-Call algorithm is 41.62% higher than the SVD++ algorithm, and more than 16.88% higher than other benchmark algorithms, the COV (Covariance, COV) algorithm is improved by more than 12.97%, the CIL algorithm is imp
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