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.

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.

Recommend Strategies for E-Learning 2.0 Communities

2014 ◽  
Vol 568-570 ◽  
pp. 1547-1550
Author(s):  
Bing Wu ◽  
Chen Yan Zhang
Keyword(s):  
Learning Communities ◽  
Knowledge Sharing ◽  
Recommendation System ◽  
Current Knowledge ◽  
Research Direction ◽  
Personalized Recommendation ◽  
Future Research ◽  
Rapid Progress ◽  
General Recommendation ◽  
E Learning

With the rapid progress of Web 2.0, E-Learning has evolved into E-Learning 2.0, which has been highlighted as an effective method for interactive learning. To improve the efficiency of learning, many researches focused on the personalized recommendation for knowledge sharing. However, these researches proposed the general recommendation system without considering the current knowledge sharing status in E-learning 2.0 communities. Therefore, the purpose of this study is to proposed recommend strategies according to characteristics of E-Learning communities based on social network analysis. Firstly, knowledge activity nodes in E-Learning communities are identified into four types. Secondly, based on four node types, E-Learning communities are classified into four corresponding types. Then, different recommend strategies are provided according to the types of E-Learning communities. Finally, conclusion and future research direction are discussed in the end.

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.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Quang-Hung Le ◽  
Son-Lam Vu ◽  
Thi-Kim-Phuong Nguyen ◽  
Thi-Xinh Le
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Contextual Information ◽  
Information Filtering ◽  
Weather Conditions ◽  
Research Literature ◽  
Future Research ◽  
Context Aware ◽  
Good Tool ◽  
Advantages And Disadvantages

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

Models of Customer Experience for B2C E-Commerce Enterprises

2016 ◽  
Vol 14 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Yilei Pei ◽  
Wanxin Xue ◽  
Dandan Li ◽  
Yong Su
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Rapid Development ◽  
Customer Experience ◽  
Service Level ◽  
Research Literature ◽  
Ease Of Use ◽  
Future Research ◽  
Customer Involvement ◽  
Acceptance Model

With the rapid development of E-commerce, more and more E-commerce enterprises attach great importance to customer experience, and B2C E-commerce enterprises make no exception. The good customer experience can promote customers' perception of the service level of B2C E-commerce enterprises. Based on previous research literature, the research elaborates effect factors of customer experience of B2C E-commerce enterprises, such as website usefulness, website ease of use, transaction costs, customer involvement and Internet word of mouth; establishes the model of customer experience of B2C E-commerce enterprises based on the technology acceptance model and points out the directions for future research.

scholarly journals A Review on Metal- and Metal Oxide-Based Nanozymes: Properties, Mechanisms, and Applications

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Qianwen Liu ◽  
Amin Zhang ◽  
Ruhao Wang ◽  
Qian Zhang ◽  
Daxiang Cui
Keyword(s):  
Metal Oxide ◽  
Catalytic Mechanism ◽  
Clinical Medicine ◽  
Low Cost ◽  
Rapid Development ◽  
Research Progress ◽  
Future Research ◽  
Chemical Production ◽  
The Past ◽  
Metal Oxide Nanomaterials

AbstractSince the ferromagnetic (Fe3O4) nanoparticles were firstly reported to exert enzyme-like activity in 2007, extensive research progress in nanozymes has been made with deep investigation of diverse nanozymes and rapid development of related nanotechnologies. As promising alternatives for natural enzymes, nanozymes have broadened the way toward clinical medicine, food safety, environmental monitoring, and chemical production. The past decade has witnessed the rapid development of metal- and metal oxide-based nanozymes owing to their remarkable physicochemical properties in parallel with low cost, high stability, and easy storage. It is widely known that the deep study of catalytic activities and mechanism sheds significant influence on the applications of nanozymes. This review digs into the characteristics and intrinsic properties of metal- and metal oxide-based nanozymes, especially emphasizing their catalytic mechanism and recent applications in biological analysis, relieving inflammation, antibacterial, and cancer therapy. We also conclude the present challenges and provide insights into the future research of nanozymes constituted of metal and metal oxide nanomaterials.

Retrospective: why do customers switch? The dynamics of satisfaction versus loyalty

2016 ◽  
Vol 30 (6) ◽  
pp. 569-575 ◽  
Author(s):  
Banwari Mittal
Keyword(s):  
Relevant Literature ◽  
Research Literature ◽  
Research Progress ◽  
Future Research ◽  
Content Type ◽  
Customer Data ◽  
High Satisfaction ◽  
Key Issues ◽  
New Research ◽  
As Relationship

Purpose This paper aims to revisit the 1998 paper (“Why do customers switch […]”) published in this journal with the goal of documenting research progress since then and identifying gaps still present in the knowledge base on the relevant key issues. Design/methodology/approach The method is literature review, theoretical scrutiny and critical reflections on the findings of the research studies over the past two decades that deal with customer satisfaction, loyalty and switching behaviors, with particular emphasis on service businesses. Findings The core issue of why satisfaction may not explain loyalty has been examined by positing other co-predictors and moderators of loyalty such as relationship quality, price value, trust, image, etc. These predictors have been found significant, implying that satisfaction is not the only driver of customer loyalty. Additionally, other drivers of switching and staying behavior have been established such as attraction of the alternatives and switching costs, respectively. This paper points out, however, that the gains of the new research literature are attenuated due to assumption of linearity in the loyalty effects of satisfaction and due to a lack of separate analyses of customer segments who defy the satisfaction–loyalty logic. Research limitations/implications The relevant literature is so vast that any account of it within the scope of this paper had to be by design delimited. The paper not only sampled the literature selectively but also summarized the principal findings of the selected papers over-simplistically. Interested readers must get a firsthand read of the reviewed literature. Practical implications The spotlight on the nonlinearity implies that practitioners should analyze customer data separately for customer segments that experience low, moderate and high satisfaction, and also separately for segments that show the expected positive satisfaction–loyalty relationship versus those who would defect despite being satisfied. Originality/value Against the backdrop where most academic as well as industry research had presumed a positive loyalty effect of satisfaction, the 1998 paper drew attention to segments of consumers who exhibited the contrarian loyalty behavior. The present paper shines a light on that topic with even sharper focus, highlighting six unaddressed issues that must frame future research.

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