Visual Analysis of the Research and Application of Learning Resource Recommendation System

With the development of information technology, the learning resource recommendation system as a means of auxiliary teaching has gradually become the research focus of many scholars in the field of education. In this paper, CiteSpace, a visualization software, is used to make a quantitative statistical analysis of journal literature related to the learning resource recommendation system in Web of Science, and four research topics are obtained: recommendation system, collaborative filtering, ontology, and E-learning. This paper summarizes the existing problems and development trends of the study of the learning resource recommendation system.

[1]  Nirmala Prakash,et al.  Improving Health Outcomes for Immigrant Families Through IPV Screening: Resources and Recommendations for Pediatric Health Care Providers. , 2019, Current problems in pediatric and adolescent health care.

[2]  Xuebin Wang,et al.  A learning resource recommendation algorithm based on online learning sequential behavior , 2019, Int. J. Wavelets Multiresolution Inf. Process..

[3]  Nataša Sokolov Milovančević,et al.  Time and ontology for resource recommendation system , 2019, Physica A: Statistical Mechanics and its Applications.

[4]  Mojtaba Salehi,et al.  Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation , 2013, Data Knowl. Eng..

[5]  Bamshad Mobasher,et al.  Resource recommendation in social annotation systems: A linear-weighted hybrid approach , 2012, J. Comput. Syst. Sci..

[6]  Korris Fu-Lai Chung,et al.  Recommender system based on scarce information mining , 2017, Neural Networks.

[7]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[8]  Chaomei Chen,et al.  CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature , 2006, J. Assoc. Inf. Sci. Technol..

[9]  Kinshuk,et al.  PLORS: a personalized learning object recommender system , 2016, Vietnam Journal of Computer Science.

[10]  Chaomei Chen,et al.  Web site design with the patron in mind: A step-by-step guide for libraries , 2006 .

[11]  Wang Chunzhi,et al.  Study of the E-learning system based on the personalized knowledge search , 2013, 2013 8th International Conference on Computer Science & Education.

[12]  Rashmi R. Sinha,et al.  The role of transparency in recommender systems , 2002, CHI Extended Abstracts.

[13]  Ahmad Baylari,et al.  Design a personalized e-learning system based on item response theory and artificial neural network approach , 2009, Expert Syst. Appl..

[14]  Mimi Recker,et al.  What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education , 2003 .

[15]  G. R. Kulkarni,et al.  Notice of Violation of IEEE Publication PrinciplesHybrid Recommender Systems: Content-Boosted Collaborative Filtering for Improved Recommendations , 2012, 2012 International Conference on Communication Systems and Network Technologies.

[16]  Noppamas Pukkhem.  LORecommendNet: An Ontology-Based Representation of Learning Object Recommendation , 2014, IC2IT.

[17]  Jingchen Liu,et al.  A reinforcement learning approach to personalized learning recommendation systems , 2018, The British journal of mathematical and statistical psychology.

[18]  Wang Heyu.  A Novel Recommendation Framework Based on Resource Flow on User-ObjectNetwork , 2013 .

[19]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[20]  Mohamed Jemni,et al.  Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[21]  Feng-Hsu Wang,et al.  Effective personalized recommendation based on time-framed navigation clustering and association mining , 2004, Expert Syst. Appl..