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- W2588543169 abstract "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." @default.
- W2588543169 created "2017-02-24" @default.
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- W2588543169 date "2017-01-01" @default.
- W2588543169 modified "2023-09-25" @default.
- W2588543169 title "Agent-Based Simulation for Agricultural Learning Resource Recommendation Based on Geographical Similarities" @default.
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- W2588543169 doi "https://doi.org/10.1007/978-3-319-52836-6_59" @default.
- W2588543169 hasPublicationYear "2017" @default.
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