Application of Transfer Learning in Task Recommendation System

被引:3
作者
Liang Ying [1 ]
Liu Boqin [1 ]
机构
[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China
来源
13TH GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT | 2017年 / 174卷
关键词
Transfer learning; Recommendation system; Mean Absolute Error; Root Mean Squared Error;
D O I
10.1016/j.proeng.2017.01.178
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In order to meet the need of individualized learning, the current network teaching platform all bring in recommendation system, including the recommendation of learning resources, the recommendation of task and exercise, etc. Problems like outdated datum or sparse datum still exist in teaching platform recommendation. Take task recommendation system of computer courses as an example, the computer knowledge updates quickly. The old knowledge will be out of date in two to three years. Another case is that, when the website was put into use, as the website traffic is quite low, the datum of task finished by the students are quite little. Both of those two cases can cause sparse datum of task system in network teaching platform. In order to solve this problem, this article tries to apply transfer learning into network teaching recommendation system, and verify its feasibility through experiment. (C) 2017 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:518 / 523
页数:6
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