Agent-Based Simulation for Agricultural Learning Resource Recommendation Based on Geographical Similarities

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• 关键词：Agent ; based simulation ; Agricultural learning ; Recommender system
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10108
• 期：1
• 页码：558-563
• 丛书名：Emerging Technologies for Education
• ISBN：978-3-319-52836-6
• 卷排序：10108

文摘

As the development of information technology and intelligent tutoring, web-based learning has been widely used nowadays to help people acquire knowledge in a flexible way. Especially, web-based learning services can greatly facilitate farmers to gain knowledge on farming. It is observed that learning goals and preferences tend to be localized for farming knowledge due to similar climate and soil conditions in an area. Therefore, geographical information can be utilized to recommend agricultural learning resources. In this paper, an agricultural learning resource recommendation approach is proposed using agent-based simulation that takes geographical information into account. The agent simulation environment is introduced. A distance-aware agent reputation model is presented. A multi-agent collaborative recommendation approach is proposed. Simulation experiments are conducted for the evaluation of the proposed approach. The results show good performance of it.