Personalized Recommendation Technology Research Articles

Theory and Practice of Constructing College Physical Education Curricula Based on Immersive Multimedia Technology

The overload of sports information resources has greatly increased the difficulty for users to choose resources. To solve this problem, this paper proposes a sports digital teaching resource recommendation model based on collaborative filtering algorithm to achieve the optimization of sports teaching mode in the context of big data and multimedia. Based on the detailed analysis of the teaching resource construction platform, the improved algorithm is applied to the teaching resource recommendation platform, and the algorithm effect is verified based on the collected data. The experimental data shows that the error of this method is significantly better than ID3 algorithm, the error is reduced by 26.55%, and the recall rate is 95.72%, which is 12.76% higher than ID3 algorithm. The introduction of personalized recommendation technology into the utilization of sports information resources can improve the efficiency and accuracy of users' access to resources and strengthen the adaptability to the continuous deconstruction and new integration of the higher education system.

The Editor and the Algorithm: Recommendation Technology in Online News

We run a field experiment to study the relative performance of human curation and automated personalized recommendation technology in the context of online news. We build a simple theoretical model that captures the relative efficacy of personalized algorithmic recommendations and curation based on human expertise. We highlight a critical tension between detailed, yet potentially narrow, information available to the algorithm versus broad (often private), but not scalable, information available to the human editor. Empirically, we show that, on average, algorithmic recommendations can outperform human curation with respect to clicks, but there is significant heterogeneity in this treatment effect. The human editor performs relatively better in the absence of sufficient personal data and when there is greater variation in preferences. These results suggest that reverting to human curation can mitigate the drawbacks of personalized algorithmic recommendations. Our computations show that the optimal combination of human curation and automated recommendation technology can lead to an increase of up to 13% in clicks. In absolute terms, we provide thresholds for when the estimated gains are larger than our estimate of implementation costs. This paper was accepted by Chris Forman, information systems. Funding: C. Peukert acknowledges funding from the Swiss National Science Foundation [Grant No. 100013_197807]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2023.4954 .

Research on Movie Reviews based on Network Communication

This article analyzes the popularity and audience scope of movie reviews, and reveals the important position of movie reviews in online communication. Conduct an in-depth analysis of the diverse forms of film reviews, including written reviews, film review videos, social media discussions, etc., and explore their impact on review content and audience engagement. Study users' participation levels, interaction forms and opinion exchanges in online movie reviews, and reveal users' roles and behavioral characteristics in reviews. It also explores the impact of fake reviews and trolls on the authenticity and credibility of reviews, as well as the impact of information overload and screening difficulties on audience selection and decision-making. Finally, an information review mechanism and personalized recommendation technology to strengthen online movie reviews are proposed to help users better filter and obtain movie review information that meets their preferences.

Research on Media resource recommendation System and recommendation Technology based on Graph Neural network

In the field of artificial intelligence research, personalized recommendation technology has been rapidly developed. It can not only provide effective recommendation according to the preferences of system users, but also sort out the historical behavior and attribute information of users, and accurately calculate the content of user preferences. From the perspective of the development of the new media industry, in order to better meet the resource needs of system users, a resource recommendation system based on personalized recommendation technology can fully meet their needs for media reading. Therefore, on the basis of understanding the current research status of media resource recommendation system and technology, and according to the basic concept of graph neural network and the main structure of recommendation system, this paper mainly studies the application effect of the recommendation model of outburst neural network that integrates knowledge graph and time weight, so as to provide effective basis for media resource recommendation in the new era.

Product Recommendation System Based on Deep Learning

Abstract With the development of Internet big data and e-commerce, the widespread popularity of information, information acquisition and personalized recommendation technologies have attracted extensive attention. The core value of personalized recommendation is to provide more accurate content and services around users. The recommended scenarios are not uniform, and different dimensions need to be considered. For example, we are facing enterprises or individuals, different age groups, different levels of education, social life and other aspects. In this paper, the classic DNN (Deep Neural Networks) double tower recommendation algorithm in the recommendation algorithm is used as the ranking algorithm of the recommendation system. It is divided into user and item for embedding respectively. The network model is built using tensorflow. The data processed by the initial data through feature engineering is sent into the model for training, and the trained DNN double tower model is obtained. Recall adopts collaborative filtering algorithm, and applies tfidf, w2v, etc. to process feature engineering, so as to better improve the accuracy of the system and balance the EE problem of the recommendation system. The recommendation module of this system is divided into data cleaning as a whole. Feature engineering includes the establishment of user portraits, the analysis of multiple recall and sorting algorithms, the adoption of multiple recall mode, and the implementation of a classic recommendation system with in-depth learning. This makes the recommendation system better balance the interests of both the platform and users, and achieve a win-win situation.

Tourism Destination Recommendation and Marketing Model Analysis Based on Collaborative Filtering Algorithm

The Internet has penetrated into all fields. As the most dynamic “sunrise industry,” tourism has also been swept into such a wave of Internet. In such an era of “information overload,” how to find one’s favorite attractions among the massive tourist attractions has become a difficult problem. In order to solve this problem, personalized recommendation technology is applied, among which collaborative filtering recommendation technology is one of the core technologies while the collaborative filtering algorithm still has problems. The research and analysis of the algorithm, this paper improves the technology for the problems of low recommendation accuracy that considers user interest changes. It for attribute scoring. It uses the multiattribute score of the item to calculate the user’s overall evaluation score of each attribute of the item; for the change of user interest, a time function based on the Ebbinghaus forgetting law is introduced to calculate the user similarity. It is given a certain weight, that is, a time function, to ultimately ensure the accuracy of the recommendation. Exploring the tourism destination recommendation and marketing model based on the collaborative filtering algorithm can enrich the relevant theories of it on the one hand, and on the other hand, it can lay the foundation for building a real tourism recommendation website.

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

With the deepening of tourists’ demand for tourism services, the personalization of online tourists has gradually become an application of personalized recommendation technology. According to the application requirements of personalized scenic spot recommendation, this paper uses social networks and Bayesian networks to fully mine the matching degree between users and scenic spots for personalized recommendation. Add social network factors to the recommendation of tourist attractions, and fully tap the social network relationship between users. First, the users are clustered by the coupling bidirectional clustering algorithm. Then, DBSCAN (density-based noise application spatial clustering) algorithm is used to cluster scenic spots. Finally, two stable user sets and scenic spot sets are applied to the personalized recommendation algorithm to predict the user’s next upcoming scenic spot. The algorithm is compared with some traditional algorithms in the dataset. The algorithm deals with the similarity of user attributes and user behavior and uses content-based algorithm to deal with the relationship between scenic spots; com datasets have better performance.

Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

In recent years, people’s living standards are rising, and the demand for health is also rising. National physical exercise has become the current trend. Using personalized recommendation technology to screen appropriate information and assist people in sports training can improve the efficiency of users to obtain relevant information and improve users’ physical quality. Therefore, the collaborative filtering recommendation algorithm is optimized by combining bisection, and the intelligent recommendation model of sports training resources is constructed based on the algorithm. This paper uses the improved algorithm to calculate the similarity between users. Compared with other traditional algorithms, the user algorithm in this paper has higher accuracy and certain reference value. To sum up, the intelligent recommendation model of sports training resources proposed in the research can more accurately recommend suitable sports training resources for users, so as to assist users in correct sports training and improve users’ physical quality, which plays an improvement of the physical quality of the whole people in China.

An E-commerce Personalized Recommendation Algorithm based on Fuzzy Clustering

The key to the performance of recommendation system lies in which recommendation technology to choose. Personalized recommendation technology has attracted the extensive attention of more and more merchants and researchers with its humanization and potential commercial value, and earliest and most widely used personalized recommendation technology. However, with the increasing number of users and commodities, the recommendation system is facing some problems, such as the difficulty of ensuring the real-time performance and the decline of recommendation quality. Many scholars put forward many solutions to the shortcomings of collaborative filtering. So as to obtain better recommendation effect. Based on different angles, this paper adopts fuzzy clustering recommendation algorithm. Using fuzzy clustering algorithm to increase population diversity can avoid the lack of diversity of adjacent users; The feature of flexible partition of fuzzy c-means clustering algorithm is used to reduce the dimension of data, so as to effectively solve the problem of recommendation quality degradation caused by data sparsity.

Recommendation of English Reading in Vocational Colleges Using Linear Regression Training Model

High-quality vocational education is a vital basis for China’s efforts to develop high-quality teachers and technical and skilled workers. The increasing development of contemporary vocational education is given higher importance in the new era. We proposed the stick to moral education while carefully integrating it with the requirements of changes in technology and modernization, continuous improvements in the quality of college vocational education, and the development of more conceptual and skilled talented individuals with both political integrity and the capacity to build modernization. Using artificial intelligence technology to challenge these difficulties in the context of social informatization can play important role in enhancing the teaching quality of high vocational education. With the advent of the Internet era, students and parents have an increasingly strong demand for online independent vocational English reading materials, although there is some English learning software that provides online vocational English reading materials in the education market. However, most of this software only changes the reading form of paper vocational English reading materials but fails to completely meet the real demand of students for vocational English reading materials, so how to carry out personalized recommendation reading according to the real reading situation and reading preferences of students is a vital problem to be resolved. In this background, this paper studies the personalized recommendation technology, in the use of recommendation algorithms on the based association rules to obtain higher vocational English reading content recommended the strongest association rules, on the based combination of user-based collaborative clarifying recommendation algorithm. The linear regression model combined with the users reading interest and personalized recommendation of higher vocational English reading process is optimized, improving the accuracy of English reading recommendation in vocational colleges.

E-Commerce Personalized Recommendation Based on Machine Learning Technology

As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of the e-commerce recommendation system and proposes the latest architecture of the e-commerce recommendation system according to the current development status of the e-commerce recommendation system. The system recommends accuracy and real-time requirements and divides the system into two parts: offline mining and online recommendation and analyzes and implements the functions and technologies of each part. User-based recommender systems, collaborative filtering recommender systems, and content-based recommender systems are analyzed, respectively. The personalized recommendation cannot only quickly help customers find the required commodity information in a wide range of complex information but also can compare more commodity information to help customers to judge. However, the existing recommendation system has some problems such as the lack of recommendation personality, the reduced relevance of recommendation, and the poor timeliness of recommendation. Finally, a recommendation system that combines three recommendation algorithms is designed, and experiments are carried out. The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode.

Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm

With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people’s lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of “information overload.” In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user’s preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy.

E-Commerce Recommendation Technology Based on Collaborative Filtering Algorithm and Mobile Cloud Computing

Personalized recommendation technology, as one of the core technologies of an E-commerce platform, has attracted a lot of attention with the rise of E-commerce in the Internet industry. Mobile cloud computing-based E-commerce has also exploded in popularity. You can buy whatever you want without leaving the house. Consumers are becoming increasingly receptive to online shopping as a result of this convenience; the E-commerce model demonstrates great modern business value. With its convenient and quick characteristics, online shopping has become fashionable and trendy; however, the popularity of the Internet and the rapid development of E-commerce has resulted in information overload, making it difficult for users to find the goods they require among a vast amount of product information. As a result, the E-commerce recommendation system was born. However, there is currently very little in-depth research on personalized recommendation technology in the field of o2o E-commerce, and most existing recommendation algorithms need to be improved in terms of accuracy and recommendation efficiency.

Enhancing Personalized Recommendation by Transductive Support Vector Machine and Active Learning

As an important component of information service networks, personalized recommendation technology provides users with better options and enables them to obtain information anytime and anywhere. Collaborative filtering (CF) is a successful and widely used form of this technology. However, the traditional CF recommendation algorithm is ineffective in environments with frequent entry of new users and high levels of data sparsity. For new users in the system, few or no scores, labels, or other such information is available, leading to the user cold start problem. Simultaneously, data sparsity leads to the selection of unreasonable neighbors, which reduces the recommendation accuracy. In addition, the traditional CF recommendation algorithm ignores the inherent connections between users’ preferences and their basic information (such as demographics). Users with similar demographic information are likely to have similar preferences, which can serve as a good basis for finding neighbors. To address the aforementioned problems, we propose a recommendation model that combines active learning (AL) and a semi-supervised transductive support vector machine (TSVM). To enable neighbors to be found quickly and accurately, similar users are clustered together on the basis of their basic information. Then, the TSVM-based classifier is trained on each cluster. To improve the quality of sample labeling and thus the classifier performance, an active learning method based on the distance strategy and a multiclassifier voting mechanism is implemented. Finally, the TSVM-based recommendation model is trained on the labeled samples. The extensive experiments conducted using a real data set from MovieLens demonstrate that the proposed model effectively alleviates the aforementioned cold start and data sparsity problems.

Personalized Recommendation for Mobile Internet Wealth Management Based on User Behavior Data Analysis

Economic development has provided good opportunities for the development of securities companies. Similarly, the development of Internet technology has also brought huge opportunities and challenges to the development of securities companies. Aiming at the current wealth management issues in the era of mobile Internet, this article attempts to develop a personalized recommendation approach on the basis of users’ behavioral data analysis. We analyzed and judged the current situation of mobile Internet wealth management using personalized recommendation systems. On the basis of personalized recommendation, we use the user’s interest tags, personalized recommendation technology, and data mining technology to analyze and summarize customer transaction records. This is done through the use of preservation of customer transaction data. By understanding customers’ investment needs, risk preferences, and other information, we can segment customers and provide them with targeted products and services. As a result of the study, a flexible personalized recommendation framework is designed and validated for mobile Internet wealth management services. The effectiveness of the proposed approach is verified through testing of the developed model.

Construction of E-commerce Personalized Information Recommendation System in the Era of Big Data

With the continuous expansion of the scale of e-commerce, personalized recommendation technology has been widely used. However, the traditional recommendation system has been unable to meet the current needs of data processing, and good big data processing ability has become the basic requirement of the new personalized recommendation system. In addition, traditional recommendation systems are often limited to tangible goods recommendation, and pay less attention to e-commerce logistics service recommendation. In this paper, through the in-depth study of information personalized recommendation service in e-commerce environment, combined with the application background of big data: Taking the user dissimilarity matrix as the recommendation model, we propose IU usercf and UDB slope one recommendation algorithm. The two algorithms based on incremental update recommendation model have good scalability, can effectively deal with big data, and have high prediction accuracy. The proposed algorithm is applied to the actual system, taking e-commerce logistics service as the recommendation object and iu-usercf as the recommendation algorithm, the personalized recommendation system for e-commerce logistics service is constructed. The e-commerce logistics service recommendation system explores the application practice of recommendation algorithm under big data, and enriches the application scenarios of personalized recommendation technology.

Mainstream Value Information Push Strategy on Chinese Aggregation News Platform: Evolution, Modelling and Analysis

At present, most news aggregation platforms use personalized recommendation technology to push information in China, which is likely to cause the phenomenon of information cocoons. In order to alleviate the occurrence of this phenomenon, this paper studies the issue of mainstream value information push from different perspectives, which can be used as a supplement for personalized recommendation technology to promote the diffusion of mainstream value information. First, we constructed an evolutionary game model to simulate the game process between news aggregation platforms and users. Through the results of evolutionary analysis, the news platform can be guided at a macro level to formulate mainstream value information push strategies by adjusting model parameters. Second, we conducted research on user behavior, and the results show that different user groups have different demands for mainstream value information. Third, we constructed two models from the perspective of user demands and platform revenue. Experiments show that user sensitivity to mainstream value information σ and platform evaluation factors val are important for finding the number of mainstream information pushes on each page. Finally, we investigated the effect of the mainstream value information from Toutiao. The survey results are consistent with the viewpoints presented in this paper.

Research of Personalized Recommendation Technology Based on Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

A Novel Teaching Approach for Mobile Internet-Based Collaborative Knowledge Construction in "Teaching Management"

With the development of education, teaching, as the basic and central work at school, is conducted most frequently with the largest workload. In simple words, teaching management for the purpose of the management of teaching work is an essential content of school management, as well as a basic activity of school leaders. Therefore, more than just the basis and guarantee for the normal operation of school teaching, teaching management course plays a critical role in teacher growth and education reform, has great significance for schools. However, problems in current teaching management courses, such as lack of teacher-student interaction, single teaching content, and simple teaching methods have hindered students to develop interest in learning, and impede teachers to give full play to their educational personality. In this paper, a new collaborative knowledge construction teaching approach was applied in mobile Internet technology environment, and a blended teaching model of students’ online autonomous collaborative learning before class, knowledge internalization in class, and leak filling after class was proposed. At the same time, the personalized recommendation technology based on collaborative filtering algorithm was applied. Firstly, mobile terminal core information was collected from the mobile Internet learning platform for preprocessing, and then the processed data were identified by the collaborative filtering algorithm to recognize the intrusion data, thereby realizing the quick screening and accurate pushing of learning resources. It was found from the teaching practice that this model can effectively enhance students' interest in learning, improve learning effects and independent innovation capabilities.

Cross-border E-commerce Course Construction Based on Data Mining Algorithm

In recent years, Cross-border(CB) e-commerce(EC) has developed rapidly in our country, and the government has also introduced a series of measures to promote the development of CB EC. In the era of big data, recommendation systems are widely used in CB EC platforms, and more and more attention is paid to its personalized recommendation technology. Based on this, this paper studies the application of data mining algorithms in the construction of CB EC courses, which has certain guiding significance for the current market and enterprise development. By taking two CB EC companies as the research object, this paper uses data mining algorithms to mine and analyze the relevant data of these two EC companies, and find that a platform with a data mining system has more advantages in researching customer shopping preferences;comparing people’s satisfaction with EC companies with data mining systems and traditional companies, 83% of the participants in the experimental group were satisfied, of which 60% were very satisfied, while only 59% in the control group of people are satisfied.This shows that data mining algorithms are of great significance to the development of CB EC, and it also points out a new direction for the construction of CB EC courses, such as CB EC based on data mining algorithms. On the one hand, this will also play a positive role in the future development of CB EC.