Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network

Personalized Item Recommendation Algorithm for Outdoor Sports

With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of “information overload,” and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it.

Online Small Sample Learner Modeling and Curriculum Recommendation with Healthy Emotional Factors of College Students

Online education is a popular way for college students at present, and it is also a good compensation way to meet the special situation that traditional offline teaching cannot complete the teaching task. Traditional classroom teaching methods have been difficult to meet the learning requirements of contemporary college students, while online classroom has made up for the shortcomings of traditional classroom teaching to some extent because of its short class hours, prominent focus, and online mobile learning. First, this paper proposes an online SSL (small sample learner) model for college students to integrate healthy emotional factors. The characteristics of learners are divided into three categories: basic characteristics of learners, characteristics of behavioral factors, and characteristics of emotional factors, and the problem of solving mapping functions is transformed into the problem of solving kernel functions. Second, a novel curriculum recommendation model integrating healthy emotional factors is proposed, which fully considers the influence of user comments on similarity and transforms the similarity of users in the overall score of the project into the similarity of users in the emotional tendency of the special attributes of the project. Through the experimental evaluation, the accuracy and stability of the recommendation are greatly improved.

Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users' preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

Ideological and political teaching information management based on artificial intelligence and data security model

In order to solve the security problems of the ideological and political teaching system itself, the ideological and political teaching information management technology needs to be further improved. According to actual needs, based on artificial intelligence and data security models, this paper studies and implements a network security information management system based on artificial intelligence and security models. The system can effectively manage the contents of safe work and increase the ability of information sharing and collaborative work. According to the actual needs of most current systems, with data mining, data recognition, and security management as the goals, this paper builds the structure of the functional modules and adopts the function cascade to finally realize the safety information management of this system. In addition, this article designs experiments to verify the performance of the model constructed in this article. The research results show that the model has good performance and meets actual needs.

Rank algorithm of web English educational resources based on fuzzy sets and RSS

In order to improve the acquisition and recommendation effect of English education resources, this paper proposes a Rank algorithm of web English educational resources based on fuzzy sets and RSS, and deeply studies the basic principles of the algorithm and introduces several keyword extraction techniques. The user’s browsing behavior and user interest acquisition methods are classified. Researchers can plan to further explore the page ranking algorithm to improve the performance of the scheme based on the damping factor. In addition, this paper uses Web technology to acquire English education resources and build a recommendation model, and uses crawler technology to build an overall system model. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper, and analyses the experimental results by mathematical statistics. The research results show that the algorithm model proposed in this paper has significant effects and is of great significance to the acquisition and recommendation of English education resources.

Research on business English autonomous learning based on artificial intelligence and improved BP network model

Due to various factors, the learning process of business English is mostly autonomous learning. However, the traditional autonomous learning model is difficult to effectively improve the learning effect of business English. In order to improve the business English learning model, based on artificial intelligence and improved BP network model, this paper builds a business intelligence autonomous learning system with certain intelligence. Moreover, this paper designs functional modules for the characteristics of business English learners, and combines the self-learning needs to facilitate the processing of structural functions, so that students can complete the operation independently. The system sets up multiple functional modules, conducts guided recommendation learning according to the characteristics of the self-learning process, and combines the feedback system to correct the shortcomings in students’ autonomous learning. Through this system, teachers can perform a variety of operations offline and eliminate restrictions on location and teaching time. In addition, in order to verify the performance of the model, the experimental study was conducted by setting up a control group and an experimental group. The research results show that the model constructed in this paper has good performance.

Recommendation of sustainable economic learning course based on text vector model and support vector machine

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.

Learning information recommendation based on text vector model and support vector machine

The difficulty of knowledge point recommendation based on the learning diagnosis model lies in how to perform feature recognition and selection of recommended knowledge points. At present, the recommendation system has certain problems in the accuracy of recommended knowledge points. Based on this, this study mainly studies the personalized problem recommendation of middle school students in the field of education. Moreover, this study takes the answer records of students’ exercises as data, and combines the characteristics of the field of education to propose an exercise recommendation algorithm based on hidden knowledge points and an exercise recommendation method based on the decomposition of student exercise weight matrix. In addition, in order to verify the effectiveness of this research algorithm, this paper selects the accuracy rate and recall rate as evaluation indicators to analyze the recommendation results of this algorithm and the current more advanced CF algorithm, and the statistical experiment results are drawn into charts. The research results show that the method proposed in this paper has certain advantages and can be used as one of the subsystems of the learning system.

An Effective Recommender System Based on Clustering Technique for TED Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

Hybrid Recommender System Using Emotional Fingerprints Model

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.