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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 114-123    DOI: 10.11925/infotech.2096-3467.2021.0548
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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.
Key words： Digital Resources      Personalized Recommendation      Scene Analysis      Multi-Source Data      Similarity Calculation     
Received: 01 June 2021      Published: 23 December 2021
ZTFLH:  TP391  
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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Articles by authors
Yanwen Wu
Qiuting Cai
Zhi Liu
Yunze Deng

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0548     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/114

Framework of Recommendation
Construction of a Reading Scene Model
Concept and Relationship Diagram of Reading Scene Ontology
Schematic Diagram of Scene Similarity Calculation
Scene Rating Matrix
MAE of Algorithms with Different Neighbor Numbers
RMSE of Algorithms with Different Neighbor Numbers
方法 CF-Pearson CF-
Cosine		 IOS User-MRDC CF-SSC
邻居数量/MAE
邻居数量/RMSE		 30/0.703 60/0.704 50/0.699 40/0.697 20/0.688
30/0.951 30/0.947 50/0.945 50/0.931 20/0.936
The Value of MAE, RMSE and Neighbors When Reach the Optimal Accuracy
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