Course video recommendation with multimodal information in online learning platforms: A deep learning framework

With the rapid development of online learning platforms, learners have more access to various kinds of courses. However, they may find it difficult to make choices due to the massive number of courses. The main contribution of our research is the design of a course recommendation framework which extracts multimodal course features based on deep learning models. In this framework, different kinds of information of course, such as course title, and course audio and course comments, are used to make proper recommendation in online learning platforms. Moreover, we utilize both explicit and implicit feedback to infer learner?s preference. Based on real-world datasets, our empirical results show that the proposed framework performs well in course recommendation, achieving an AUC score of 79.03%. This framework can provide technical support for course video recommendation, thus helping online learning platforms to manage course resources and optimize user learning experience.

[1]  Guoqing Chen,et al.  Deep learning based personalized recommendation with multi-view information integration , 2019, Decis. Support Syst..

[2]  Kai Chen,et al.  Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..

[3]  Kay Livingston,et al.  Blending online learning with traditional approaches: changing practices , 2007, Br. J. Educ. Technol..

[4]  Yoneo Yano,et al.  A framework of context-awareness support for peer recommendation in the e-learning context , 2007, Br. J. Educ. Technol..

[5]  Xiaoyong Du,et al.  Analogical Reasoning on Chinese Morphological and Semantic Relations , 2018, ACL.

[6]  S. B. Aher,et al.  Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data , 2013, Knowl. Based Syst..

[7]  Jeongwan Kang,et al.  Implementing a case-based e-learning environment in a lecture-oriented anaesthesiology class: Do learning styles matter in complex problem solving over time? , 2009, Br. J. Educ. Technol..

[8]  Synnöve Kekkonen-Moneta,et al.  E-Learning in Hong Kong: comparing learning outcomes in online multimedia and lecture versions of an introductory computing course , 2002, Br. J. Educ. Technol..

[9]  Jason K. Y. Chan,et al.  Direct and indirect effects of online learning on distance education , 2004, Br. J. Educ. Technol..

[10]  Jie Lu,et al.  A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system , 2015, Decis. Support Syst..

[11]  John Morgan,et al.  Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision , 2015, Br. J. Educ. Technol..

[12]  Jialie Shen,et al.  On Effective Location-Aware Music Recommendation , 2016, ACM Trans. Inf. Syst..

[13]  Minjuan Wang,et al.  Designing online courses that effectively engage learners from diverse cultural backgrounds , 2007, Br. J. Educ. Technol..

[14]  Jie Zhou,et al.  Dynamic Personalized Recommendation on Sparse Data , 2013, IEEE Transactions on Knowledge and Data Engineering.

[15]  Xin Li,et al.  A multi-theoretical kernel-based approach to social network-based recommendation , 2014, Decis. Support Syst..

[16]  Kirk Perris,et al.  Online tutorial support in open and distance learning: students' perceptions , 2005, Br. J. Educ. Technol..

[17]  LuJie,et al.  A semantic enhanced hybrid recommendation approach , 2015 .

[18]  Edwin R. Griff,et al.  Evaluation of an adaptive online learning system , 2013, Br. J. Educ. Technol..

[19]  Mei Liu,et al.  Using the Facebook group as a learning management system: An exploratory study , 2012, Br. J. Educ. Technol..

[20]  Steven Warburton,et al.  Second Life in higher education: Assessing the potential for and the barriers to deploying virtual worlds in learning and teaching , 2009, Br. J. Educ. Technol..

[21]  Chen-Chung Liu,et al.  Knowledge exploration with concept association techniques , 2010, Online Inf. Rev..

[22]  Zhoujun Li,et al.  Video recommendation over multiple information sources , 2012, Multimedia Systems.

[23]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Lei Shi,et al.  Local Representative-Based Matrix Factorization for Cold-Start Recommendation , 2017, ACM Trans. Inf. Syst..

[25]  Juntao Liu,et al.  Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation , 2013, Decis. Support Syst..

[26]  Riina Vuorikari,et al.  Collaborative recommendation of e-learning resources: an experimental investigation , 2010, J. Comput. Assist. Learn..

[27]  Till Becker,et al.  Social Context-Aware Recommendation for Personalized Online Learning , 2017, Wireless Personal Communications.