The multimedia recommendation algorithm based on probability graphical model

Li, Chen; Liu, Yu; Wang, Chunhua; Dong, Shifeng; Gao, Haofei; Zhao, Qian; Wu, Wei

doi:10.1007/s11042-020-10129-8

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…According to the different types of learning resources, learning objectives, and student groups, designing a flexible personalized recommendation model of learning resources has become a breakthrough to solve this educational problem [6,7]. is study proposes an intelligent recommendation of educational resources based on user model, which aims to mine educational resources and learning partners that meet the individual needs of learners from massive educational data, recommend learning activities that adapt to learners' cognitive styles, and provide them with adaptive and personalized educational services [8].…”

Section: Prefacementioning

confidence: 99%

[Retracted] Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Zhang

2022

Journal of Mathematics

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Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

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Section: Prefacementioning

confidence: 99%

[Retracted] Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Zhang

2022

Journal of Mathematics

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“…e nodes are no longer classified. If the purity is high, the above two leaf nodes will be used as a new set and continue to be split until the purity is small enough to meet the standard [19].…”

Section: Introduction To Random Forest Algorithmmentioning

confidence: 99%

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

Gao

Ding

2022

Security and Communication Networks

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The traditional marketing model can no longer meet the needs of users and can not add more benefits to the enterprise, and digital marketing came into being. At present, most of the marketing focus of various enterprises is still mainly on products, and the reflection arc to market changes is long. Therefore, the formulation of marketing activities should always pay attention to changes in user needs and combine corresponding activity planning, product planning, brand building, etc., according to Changes in the target market adjust the content of marketing activities and products in real-time and, at the same time, pay attention to user feedback on products in order to iteratively update products in time, improve product market competitiveness, and optimize the user experience. In this paper, through the study and research of the traditional random forest method and some data processing algorithms, the feature selection and class imbalance problems of random forest are improved, respectively. Through the study of feature selection methods, we can maintain a balance between feature strength and relevance during feature selection and improve the final model classification effect. And through the research and experiment of the imbalanced data classification problem and the random forest algorithm, the method of the random forest model to deal with the imbalanced problem has been improved. After experimental calculation and analysis, it is found that for the effect of the minimum number of samples required for node splitting with different numbers, the best results are obtained when 2 samples are taken as the minimum number of samples required for node splitting, and the average value of the F1 evaluation is 0.1038; for different specifications, the effect of the random forest is the best using the Gini index, and the average value of its F1 evaluation is 0.1033; for the effect analysis of random forests with different numbers of trees, 7 to 10 decision trees are the best, and the F1 evaluation is the best. The average is 0.10175.

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“…They verified that customer loyalty can improve the product recommendation accuracy in random walking between nodes in a network. To realize multimedia recommendation, Li et al [56] proposed adding user labels to the model for cold start and data sparse problems involved in CF recommendation, and used the random walking-based PersonalRank algorithm to calculate the weight coefficients of user labels. The probabilistic graph multimedia recommendation algorithm is then improved by dimensionality reduction and clustering.…”

Section: B Bipartite Graph Modelmentioning

confidence: 99%

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

2022

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The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers' characteristics, such as educational background, degree, professional title, age, teaching age, job burnout, and academic research. However, because these characteristics are mostly evolvable, research findings are inconsistent. Therefore, we attempted to identify the association between teaching styles that reflect teachers' stable psychological quality, Technological Pedagogical Content Knowledge (TPACK), and teaching quality. To this end, we first collected data from three different disciplines at a university using the constructed teaching quality, TPACK, and course difficulty questionnaires, together with the TSTI scale proposed by Grigorenko and Sternberg. We constructed three matrices with different sparsities as experimental datasets using teachers with the teaching style and PTACK attributes, courses with the course difficulty attribute, and teaching quality. We then constructed a weighted bipartite graph with the teachers and courses in the matrix as nodes and the teaching quality divided by course difficulty as the weights of the edges. We proposed an improved Slope One algorithm based on a weighted bipartite graph to scientifically predict teachers' teaching quality in untaught courses. Finally, we constructed a TOP-N recommendation model for course teachers that combined teaching style and TPACK features to achieve accurate recommendations for course teachers. The experiments show that our proposed solution is feasible and that the algorithmic model is effective. Therefore, we developed a scientific method to improve the quality of university course teaching.

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The multimedia recommendation algorithm based on probability graphical model

Cited by 3 publications

References 31 publications

[Retracted] Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

[Retracted] Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Construction of Digital Marketing Recommendation Model Based on Random Forest Algorithm

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

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