scholarly journals Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaohua Fang ◽  
Qiuyun Lu
Keyword(s):  
Learning Process ◽  
Data Science ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
Learning Ability ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learner Model ◽  
Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

scholarly journals Optimization Analysis and Implementation of Online Wisdom Teaching Mode in Cloud Classroom Based on Data Mining and Processing

2021 ◽  
Vol 16 (01) ◽  
pp. 205
Author(s):  
Jing Gao ◽  
Xiao-Guang Yue ◽  
Lulu Hao ◽  
M. James C. Crabbe ◽  
Otilia Manta ◽  
...  
Keyword(s):  
Online Education ◽  
Online Teaching ◽  
Recommendation System ◽  
Rapid Development ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
Optimization Analysis ◽  
Teaching Mode ◽  
Internet Education ◽  
Effi Ciency

The rapid development of Internet technology and information technology is rapidly changing the way people think, recognize, live, work and learn. In the context of Internet + education, the emerging learning form of a cloud classroom has emerged. Cloud classroom refers to the process in which learners use the network as a way to obtain learning objectives and learning resources, communicate with teachers and other learners through the net-work, and build their own knowledge structure. Because it breaks the boundaries of time and space, it has the characteristics of freedom, high effi-ciency and extensiveness, and is quickly accepted by learners of different ag-es and occupations. The traditional cloud classroom teaching mode has no personalized recommendation module and cannot solve an information over-load problem. Therefore, this paper proposes a cloud classroom online teach-ing system under the personalized recommendation system. The system adopts a collaborative filtering recommendation algorithm, which helps to mine the potential preferences of users and thus complete more accurate recommendations. It not only highlights the core position of personalized curriculum recommendation in the field of online education, but also makes the cloud classroom online teaching mode more intelligent and meets the needs of intelligent teaching.

E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

scholarly journals A Probability-Based Hybrid User Model for Recommendation System

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Hao ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Lin Gong ◽  
Bo Zhao
Keyword(s):  
Product Design ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
User Model ◽  
Information Communication ◽  
Design Cycle ◽  
Available Information ◽  
User Ratings ◽  
Content Based Filtering

With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

2016 ◽  
Vol 16 (6) ◽  
pp. 146-159 ◽  
Author(s):  
Zhijun Zhang ◽  
Huali Pan ◽  
Gongwen Xu ◽  
Yongkang Wang ◽  
Pengfei Zhang
Keyword(s):  
Social Relations ◽  
Recommendation System ◽  
Rapid Development ◽  
Geographic Location ◽  
Real Data ◽  
Personalized Recommendation ◽  
Data Sets ◽  
Tourist Attraction ◽  
Recommendation Algorithm ◽  
Tourism Information

Abstract With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

scholarly journals 5G Multimedia Precision Marketing Based on the Improved Multisensor Node Collaborative Filtering Recommendation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Si ◽  
Min Zhou ◽  
Yingfang Qiao
Keyword(s):  
Collaborative Filtering ◽  
Social Relations ◽  
Recommendation System ◽  
Rapid Development ◽  
Auxiliary Information ◽  
Web Technology ◽  
Practical Significance ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Regularization Term

The rapid development of web technology has brought new problems and challenges to the recommendation system: on the one hand, the traditional collaborative filtering recommendation algorithm has been difficult to meet the personalized recommendation needs of users; on the other hand, the massive data brought by web technology provides more useful information for recommendation algorithms. How to extract features from this information, alleviate sparsity and dynamic timeliness, and effectively improve recommendation quality is a hot issue in the research of recommendation system algorithms. In view of the lack of an effective multisource information fusion mechanism in the existing research, an improved 5G multimedia precision marketing based on an improved multisensor node collaborative filtering recommendation algorithm is proposed. By expanding the input vector field, the features of users’ social relations and comment information are extracted and fused, and the problem of collaborative modelling of these two kinds of important auxiliary information is solved. The objective function is improved, the social regularization term and the internal regularization term in the vector domain are analysed and added from the perspective of practical significance and vector structure, which alleviates the overfitting problem. Experiments on a large number of real datasets show that the proposed method has higher recommendation quality than the classical and mainstream baseline algorithm.

Research on Location-Based Personalized Recommendation System

2014 ◽  
Vol 490-491 ◽  
pp. 1493-1496
Author(s):  
Huan Gao ◽  
Xi Tian ◽  
Xiang Ling Fu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Information Overload ◽  
Information Filtering ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
New System

With the mobile Internet developing in China, the problem of information overload has been brought to us. The traditional personalized recommendation cannot meet the needs of the mobile Internet. In this paper, the recommendation algorithm is mainly based on the collaborative filtering, but the new factors are introduced into the recommendation system. The new system takes the user's location and friends recommendation into the personalized recommendation system so that the recommendation system can meet the mobile Internet requirements. Besides, this paper also puts forward the concept of moving business circle for information filtering, which realizes the precise and real-time personalized recommendations. This paper also proves the recommendation effects through collecting and analyzing the data, which comes from the website of dianping.com.

The Study of User Model of Personalized Recommendation System Based on Linked Course Data

2014 ◽  
Vol 519-520 ◽  
pp. 1609-1612
Author(s):  
Ji Yan Wu ◽  
Chan Le Wu
Keyword(s):  
Linked Data ◽  
Recommendation System ◽  
User Model ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Path ◽  
E Learning ◽  
Linked Courses ◽  
Efficient Learning ◽  
Massive Information

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc .Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.

Research on the Key Technology of Personalized Learning for Peasant Based on Wireless Network

2014 ◽  
Vol 568-570 ◽  
pp. 1577-1580 ◽  
Author(s):  
Ji Chun Zhao ◽  
Shi Hong Liu ◽  
Jian Xin Guo ◽  
Zhu Feng Qiao
Keyword(s):  
Wireless Network ◽  
Communication Networks ◽  
Recommendation System ◽  
Rapid Development ◽  
Personalized Learning ◽  
Research Literature ◽  
Research Progress ◽  
Personalized Recommendation ◽  
Future Research ◽  
Key Technology

With the rapid development of wireless communication networks and mobile terminal technology, it has an important significance for improving the cultural quality of farmers and the rural information level that researching the key technology of personalized learning for farmers based on wireless network. The research progress of personalized service was introduced based on reading a large number of domestic and foreign related research literature. The personalized recommendation technology, the recommendation algorithm and measure method was given in the research of personalized technology. The personalized recommendation system was designed and tested, the result could need the users need. Finally the problems and future research directions in the personalized system is summarized.

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