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Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation |
Wu Yanwen1,2,Cai Qiuting2( ),Liu Zhi3,Deng Yunze2 |
1National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China 2College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China 3National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, China |
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Abstract [Objective] This paper proposes a new method based on multi-source data fusion and scene similarity calculation to accurately recommend digital resources for users. [Methods] First, we constructed a scene model integrating multi-source data, and obtained their abstract representation. Then, we calculated the scene similarity based on the detailed similarity index. Finally, we predicted the scene list and corresponding resources according to their similarity level predictions, and optimized the recommendation results. [Results] Compared with CF Pearson, CF cosine, IOS and user-MRDC, the proposed CF-SSC algorithm performed best on the index MAE (0.688), and was slightly inferior to user-MRDC on the index RMSE (0.936). It required the least number of neighbors (20) to reach the optimal value of MAE and RMSE. [Limitations] Our new algorithm was only tested with small data sets. [Conclusions] The proposed similarity algorithm improves the prediction accuracy and the effectiveness of resource recommendation system.
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Received: 01 June 2021
Published: 23 December 2021
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Fund:National Natural Science Foundation of China(61937001);National Emerging Engineering Education Research and Practice Project(E-RGZN20201032);Hubei Provincial Teaching Research Project(2020139) |
Corresponding Authors:
Cai Qiuting,ORCID:0000-0002-8359-0776
E-mail: 1206948864@qq.com
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