Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)

Micro Course Recommendation Model Optimization based on User Behavior

Authors
Xiangbin Gao, Lian Wang
Corresponding Author
Xiangbin Gao
Available Online April 2017.
DOI
https://doi.org/10.2991/iemss-17.2017.165How to use a DOI?
Keywords
Recommendation Model; Search Engine; Micro Course; User Interest.
Abstract
With the advent of the information era, it has brought a change to the education industry. As the network educational resources gradually increased, to obtain individual needs in the massive resources has become a headache problem for users. In the use of traditional search engines for information retrieval, users need to actively input the retrieval keywords, and the search results have no users individualization feature. In this case, recommendation technology has emerged as a complementary technique for search engine. In this paper, we combined recommendation technology with micro course system in the education field, through the establishment of user interest model, at last made use of the recommendation algorithm based on users as the bridge to link the user interest and micro course video resources, presented the video resources that users are satisfied with and interested in to the user, and completed personalized video recommendation. The combination of the recommendation model and the micro course has a positive effect on reducing the time needed to retrieve the video resources and improving the learning and working efficiency of teachers and students.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Xiangbin Gao
AU  - Lian Wang
PY  - 2017/04
DA  - 2017/04
TI  - Micro Course Recommendation Model Optimization based on User Behavior
BT  - Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)
PB  - Atlantis Press
SP  - 843
EP  - 849
SN  - 2352-5428
UR  - https://doi.org/10.2991/iemss-17.2017.165
DO  - https://doi.org/10.2991/iemss-17.2017.165
ID  - Gao2017/04
ER  -