scholarly journals Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Nan Wang
Keyword(s):  
Analytic Hierarchy Process ◽  
Collaborative Filtering ◽  
Missing Values ◽  
Recommendation System ◽  
Ideological And Political Education ◽  
Political Education ◽  
Analytic Hierarchy ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Hierarchy Process

Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.

Ideological and political teaching model using fuzzy analytic hierarchy process based on machine learning and artificial intelligence

2020 ◽  
pp. 1-13
Author(s):  
Yuxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Analytic Hierarchy Process ◽  
Teaching Quality ◽  
Ideological And Political Education ◽  
Fuzzy Analytic Hierarchy Process ◽  
Political Education ◽  
Current Status ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Ideological and political education plays an important role in supporting social talent input. However, the current evaluation effect of ideological and political education is difficult to quantify. Therefore, in order to improve the evaluation effect of ideological and political education, based on artificial intelligence algorithms, this study combines machine learning ideas and the current status of ideological and political education to build a fuzzy analytic hierarchy process model of the of ideological and political teaching quality based on machine learning and artificial intelligence. Moreover, this study uses a three-tier structure to build a model network structure, and based on the characteristics of fuzzy evaluation, this study uses the expert system to conduct data management, operation and control of model evaluation, and build a corresponding database to update the data in real time. In addition, in order to verify the effect of the model, this study sets simulation experiments to analyze the model performance. From the point of view of running effect and running speed, this research model meets the actual needs of the system, so it can be applied to the evaluation process of ideological and political teaching quality in colleges and universities.

scholarly journals Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

Job Recommendation System Based on Analytic Hierarchy Process and K-means Clustering

2021 ◽  
Author(s):  
Peini Feng ◽  
Charles Jiahao Jiang ◽  
Jiale Wang ◽  
Sunny Yeung ◽  
Xijie Li
Keyword(s):  
Analytic Hierarchy Process ◽  
Recommendation System ◽  
Analytic Hierarchy ◽  
Job Recommendation ◽  
Hierarchy Process

An Electronic Commerce Recommender System Based on Product Character

2011 ◽  
Vol 267 ◽  
pp. 909-912 ◽  
Author(s):  
Shen Bao Chen
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Competitive Environment ◽  
Referral Process ◽  
Personalized Service ◽  
Filtering Algorithm ◽  
Customer Churn ◽  
Collaborative Filtering Algorithm

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

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