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
[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.
吴彦文, 蔡秋亭, 刘智, 邓云泽. 融合多源数据和场景相似度计算的数字资源推荐研究*[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation. Data Analysis and Knowledge Discovery, 2021, 5(11): 114-123.
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