Personalized Recommendation Algorithm Research Articles

Personalized Recommendation Algorithm in Computer-Aided Music Instruction System

Personalized Recommendation Algorithm in Computer-Aided Music Instruction System

A Personalized Recommendation Algorithm for Tourist Attractions Using User Behavior Data

With the widespread application of 5G and cloud computing technologies, smart tourism has become a hotspot and development trend in the tourism industry. This paper develops a cloud-based intelligent tourism application for the Android platform, innovatively integrating edge computing technology to achieve faster data processing speeds and lower latency, providing users with instant travel information and services. The application adopts a personalized recommendation algorithm to intelligently recommend tourist attractions and routes based on user behavior and preferences, and introduces a geo-tagged social function, allowing users to share travel experiences and reviews, enhancing user interaction and community sense. Furthermore, the application incorporates augmented reality (AR) technology, providing virtual tour guide services through the mobile camera, bringing users an immersive travel guide experience. This paper details the design architecture, key technology implementation, and expected effects of this intelligent tourism application, aiming to enhance the tourism experience while promoting the digital transformation and development of the tourism industry through technological innovation.

Research on Hybrid Recommendation Algorithm based on Collaborative Filtering and Spearman Rank Correlation Coefficient

A recommendation system is an information filtering tool that helps users find the products or services they need from a large amount of information. However, collaborative filtering is quite sensitive to data sparsity and cold start problems, and it may encounter certain difficulties when handling outliers. To address the issue of handling outliers, it is necessary to study and improve the existing collaborative filtering techniques. This paper proposes a personalized recommendation algorithm that integrates collaborative filtering with the Spearman rank correlation coefficient. By combining collaborative filtering and the Spearman rank correlation coefficient, the algorithm uses the latter to handle outliers, making it more suitable for nonlinear relationships. This hybrid recommendation algorithm can better handle outliers while maintaining personalized recommendations, providing a basis and reference for personalized recommendations.

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

This paper aims to explore the design and implementation of product demand forecasting and personalized recommendation system based on data analysis. Through the collection and analysis of a large number of user behavior data, an accurate demand prediction model is constructed, and based on this, a personalized recommendation algorithm is implemented. The paper first introduces the research background and purpose, and then elaborates the research method, process and results. The method based on data analysis can effectively predict the product demand and provide users with personalized recommendation services, so as to improve user satisfaction and market competitiveness of enterprises.

Application of personalized recommendation algorithm based on Sensor networks in Chinese multimedia teaching system

The development of modern educational methods and educational technology has promoted the development of Chinese language teaching, and this teaching method has many advantages over traditional teaching. In order to extend and develop the teaching of Chinese language majors, this article designs a multimedia type system based on Chinese language teaching by combining data mining technology and recommendation algorithms. This system requires the use of a series of related technologies in the application process, the core of which is data mining technology. This article analyzes this system from different research perspectives and considers it from the user's perspective to improve and optimize the information resource library. And according to the needs of some users for the Chinese language multimedia system, the functions were added. Finally, the relevant experimental data was weighted and the final results were obtained. It was found that it can greatly improve the efficiency of the original teaching system. After experimental comparison, it was found that the improved teaching system can quickly complete some teaching tasks, improve overall work efficiency, and lay a theoretical and technical foundation for the subsequent establishment of a Chinese language multimedia teaching system.

Research on the Application of Big Data and Artificial Intelligence in Search Engine

With the development of the Internet, search engine as an important tool of information retrieval, more and more need to deal with large-scale, complex data. The combination of big data and artificial intelligence technology provides a new solution for search engine optimization. This paper mainly discusses the application of big data and artificial intelligence in search engine, including data collection and processing, data mining and pattern recognition, user behavior analysis, personalized recommendation algorithm, machine learning algorithm, deep learning algorithm, reinforcement learning algorithm and the intelligence of personalized recommendation system.

XAI Personalized Recommendation Algorithm Using ViT and K-Means

XAI Personalized Recommendation Algorithm Using ViT and K-Means

Research on the Teaching Reform Path of Art and Design Specialties in Colleges and Universities in the Era of Artificial Intelligence

Abstract In this study, the ability level of art and design students in colleges and universities was accurately calculated by adopting the Logistics model in item response theory, combined with learner-question response data, and the maximum likelihood estimation method. Further, the study constructs a personalized recommendation model based on students’ competence, which utilizes the singular value decomposition technique to calculate students’ similarity coefficients, and achieves accurate personalized learning path recommendation. Through descriptive analysis, this paper delves into the specific application and effect of this personalized recommendation algorithm in the teaching reform of art and design majors in colleges and universities. The simulation analysis method evaluates the impact of online art and design professional teaching. The results show that this algorithm performs best in scoring prediction accuracy when the regularization coefficient is set to 0.02, and its RMSE value reaches 0.95012.This finding confirms that the personalized recommendation algorithm based on the students’ ability can effectively promote the reform of art and design teaching in colleges and universities, and realize tailored teaching. In addition, through accurate learning path recommendation, the algorithm significantly improves students’ learning ability and enthusiasm and provides valuable guidance and reference for teaching reform in colleges and universities.

Research on Practical Innovation of Elderly Education Service System in the Era of Artificial Intelligence

Abstract The trend of our country into an aging society is becoming more and more obvious, and the elderly education service has become the focus of social attention. The integration of artificial intelligence into elderly education is one of the inspiring ideas in which the personalized recommendation algorithm can recommend educational resources according to the characteristics of older people, and it has the prospect of application. In this paper, we first provide a CTransD-GAT recommendation model based on a knowledge graph, which improves traditional problems such as data sparsity by setting weight preference and feature aggregation. A dynamic preference-capturing method is proposed based on contextual interaction to capture the variable user learning interests more accurately and flexibly. This paper examines the practical utility of personalized recommendation methods for educational resources based on these two improved techniques. The post-test mean score of each knowledge module test of the experimental group is improved by 1.83 points compared with the pre-test, 11.46 points improve the score of teaching ability, and the scores of perceived usefulness, ease of use, and intention to use are 3.82, 3.89, and 3.97, respectively. It shows that the improved educational resource recommendation model has an excellent effect on improving knowledge structure and teaching ability, and it is characterized by simplicity and ease of use.

Research on optimization strategy of new media marketing of rural tourism under the background of “Internet+”

Abstract Under the background of the Internet, the effective use of new media means can improve the marketing level of rural tourism and adapt to the development needs of the new era. The construction of a new media marketing platform for rural tourism uses Hadoop, an extensive data framework system, to analyze the tourism preferences of various consumers. Based on the constructed platform, a rural tourism consumer portrait model is designed using the fuzzy-C mean clustering algorithm and the personalized recommendation algorithm considering the time effect to realize the precise marketing of rural tourism consumers. In the analysis of consumer precision marketing, consumers can be subdivided into three categories of growing, high-value and general consumers through clustering, and the marketing success rate of the recommendation algorithm in this paper reaches 0.765. The regression analysis further understands that the quality of the products and services of the rural tourism website (0.586) is the factor that has the most significant influence on the satisfaction of the consumers, followed by brand awareness (0.556), and that the different new rural tourism media marketing strategies have the most significant influence on the satisfaction of consumers. Consumer satisfaction is affected by media marketing strategies in other ways. Based on the study’s results, this paper proposes relevant optimization of new media marketing strategies for rural tourism to promote sustainable development.

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

Personalized Recommendation Algorithm Based on Trajectory Mining Model in Intelligent Travel Route Planning

The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence

Abstract While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service quality. Against the background of the artificial intelligence era, the article successfully constructs a framework for an office reception work management system by utilizing cutting-edge React and Express technologies. The system not only carries out meticulous module design based on functional requirements, but also proposes a personalized recommendation algorithm integrating clustering and collaborative filtering by optimizing the Similarity of user characteristics and item attributes for joint filtering recommendation to achieve more humanized reception services. To verify the system's effectiveness, this study conducted an application analysis using University Q as a case study. The analysis results show that from 2008 to 2022, the indices of reception preparation, knowledge of reception work, staffing, business skills of reception staff, work attitude and work detail control have been significantly improved, with an increase ranging from 32.0761 to 37.1677, indicating that the management level of office reception work is constantly optimized and shows a rapid growth. This innovative office reception work management system not only realizes refined management, but also comprehensively improves the service efficiency and comprehensive management level, which provides a valuable reference for the management of similar work.

基于大数据的电子商务个性化推荐算法研究与合法性探析

基于大数据的电子商务个性化推荐算法研究与合法性探析

Construction and Sharing Mechanism of Digital Vocal Music Teaching Resource Library

Abstract With the rapid development of information technology, the construction and sharing mechanism of the teaching resource base has become an important means of informatization of vocal music education. The article proposes the construction and sharing of a personalized teaching resource system based on a collaborative filtering recommendation algorithm, constructs a personalized recommendation module through the study of collaborative filtering, and further improves the personalized recommendation algorithm. Then, it elaborates on the relevant contents of user interest modeling, records and manages the user's interest, describes the user's potential interest needs, records the user's behavior by building a model, and finally analyzes the application of the teaching resource library. When the boundary value of request concurrency does not exceed 1000, the response time of the teaching resource library page is no more than 2.5 seconds. When the teaching resource library is used to process a file of 100MB size, the upload speed of the WebSocket method is 153s faster than FancyUpload. More than 70% of the students think that the use of the teaching resource library in the vocal music class can greatly enhance the effect of vocal music learning.

BiVAE를 활용한 MBTI 기반 OTT 서비스 개인화 추천 시스템

The OTT platform, which provides various media contents in the modern society, seeks to improve the user experience by utilizing a personalized recommendation system. However, current recommendation systems are having difficulty in making accurate recommendations because they do not fully understand user's tendencies. This study proposes the use of a personalized recommendation algorithm using BiVAE (bilateral variational autoencoder) to solve this problem, and considers group-specific applications through MBTI. BiVAE is a probabilistic generation model that compresses and generates data considering of bidirectional information flow, providing richer expression and reconstruction capabilities by encoding and decoding in both directions. As a result of applying four types of data to the model, when applying BiVAE for each group using MBTI, the accuracy of OTT genre recommendation was shown to be superior to that of other methods. In addition, when considering MBTI, a personal recommendation using BiVAE showed higher accuracy compared to the existing recommendation system. This result means that the personalized recommendation system considering the user's personal characteristics and personality is important.

An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.

Retracted: An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

Retracted: An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

Abstract In the development process of education informatization, realizing personalized learning is the urgent demand of learners. In this paper, we construct an online teaching resources recommendation algorithm (VGNN) based on a graph neural network (GNN). The project-based collaborative filtering algorithm is used to find highly similar teaching resource materials using Pearson’s correlation coefficient in order to personalize the recommendation of online teaching resources. The personalized recommendation algorithm for online teaching resources was tested and analyzed using the online teaching course Fundamentals of Vocal Music Theory as a case study. The results show that the course “Fundamentals of Vocal Music Theory” is divided into 4 clusters with clear boundary contours under the graph neural network algorithm, and the number of knowledge points in the 4 clusters is 2308, 655, 513, and 97, respectively. After learning the personalized recommended content from the 4 clusters of knowledge points, the average rate of loss of points for each of them decreases by about 0.3. The algorithm effectively solves the accurate classification of vocal knowledge and implements personalized recommendations for educational resources.

Research on the Design of Digital Teaching Resource Management and Sharing Platform in Colleges and Universities Based on Cloud Storage

Abstract In this paper, after analyzing the structural system of the digital resource sharing model in colleges and universities, a shared storage model based on cloud storage is designed, and a digital teaching resource management and sharing platform for colleges and universities based on cloud storage is constructed on this basis. In order to realize accurate sharing, the sharing behaviors of teaching resources are symbolically illustrated, double behavior aggregation is performed, and a multidimensional preference personalized recommendation algorithm, i.e., DB-CGAT model, is designed to construct the personalized sharing module of teaching resources that integrates the behavioral graph. In addition, three types of experiments are conducted on the personalized sharing module of the platform to test its sharing performance and overall performance. The results show that the accuracy of this model is reduced by 0.04 compared with the traditional algorithm; the 90 percent time of login transaction is 2.44, and the time of query transaction is 1.46, which is all up to the standard value. All the action actions are green, and the number of clicks and throughputs in 5~30ss are around 120. Universities can benefit from the digital teaching resource management and sharing platform designed in this paper, which can improve teaching efficiency.

Relationship Between Doom-scrolling and Mental Health under the Influence of Recommendation Algorithms

Doom-scrolling is an entirely new concept in the study of mental health and has attracted considerable societal attention over the past few years. This paper is divided into two studies on the relationship between doom-scrolling and mental health: Study 1 explained how the doom-scrolling phenomenon occurs by analyzing the underlying logic of the social media personalized recommendation algorithm, while in Study 2, 15 items of the Doom-scrolling Scale were confirmed by confirmatory factor analysis. Different reliability coefficients support the scales high reliability and prove the scales applicability to the target group. In Study 2, it was further explained that there is a significant negative impact relationship between misfortune rolling and mental health of happiness index. The structural equation model shows a significant negative relationship between doom-scrolling and mental health. This pioneering research on doom-scrolling emphasizes the impact of this concept on both individuals and society.