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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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[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 wordsDigital 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:

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.

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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-
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
[1] 杨君, 吴菊华, 艾丹祥. 一种基于情景相似度的多维信息推荐新方法研究[J]. 情报学报, 2013, 32(3):262-269.
[1] (Yang Jun, Wu Juhua, Ai Danxiang. A New Method for Multi-Dimensional Information Recommendation Based on the Similarity of Context[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(3):262-269.)
[2] 张明红, 佘廉, 耿波. 基于情景的结构化突发事件相似度研究[J]. 中国管理科学, 2017, 25(1):151-159.
[2] (Zhang Minghong, She Lian, Geng Bo. Research on the Similarity of the Structured Emergency Events Based on Scenario[J]. Chinese Journal of Management Science, 2017, 25(1):151-159.)
[3] 曾子明, 陈贝贝. 移动环境下基于情境感知的个性化阅读推荐研究[J]. 情报理论与实践, 2015, 38(12):31-36.
[3] (Zeng Ziming, Chen Beibei. Research on Personalized Reading Recommendation Based on Context-aware in Mobile Environment[J]. Information Studies: Theory & Application, 2015, 38(12):31-36.)
[4] 谭剪梅. 顾及多类型用户需求的地震灾害场景知识图谱构建及应用[D]. 成都: 西南交通大学, 2019.
[4] (Tan Jianmei. The Construction and Application of Earthquake Disaster Scene Knowledge Graph Concerned on the Demands of Multy-type Users[D]. Chengdu: Southwest Jiaotong University, 2019.)
[5] 刘华真, 王巍, 谷壬倩, 等. 基于用户浏览行为的个性化推荐研究综述[J]. 计算机应用研究, 2021, 38(8):2268-2277.
[5] (Liu Huazhen, Wang Wei, Gu Renqian, et al. Survey of Personalized Recommendation Study Based on User Browsing Behavior[J]. Application Research of Computers, 2021, 38(8):2268-2277.)
[6] 刘嘉, 都兴中, 陈振宇, 等. 移动推荐研究综述[J]. 情报科学, 2012, 30(10):1584-1590.
[6] (Liu Jia, Du Xingzhong, Chen Zhenyu, et al. A Survey of Research on Mobile Recommendation[J]. Information Science, 2012, 30(10):1584-1590.)
[7] 周明建, 赵建波, 李腾. 基于情境相似的知识个性化推荐系统研究[J]. 计算机工程与科学, 2016, 38(3):569-576.
[7] (Zhou Mingjian, Zhao Jianbo, Li Teng. Personalized Knowledge Recommendation System Based on Context Similarity[J]. Computer Engineering & Science, 2016, 38(3):569-576.)
[8] 杨峰, 张月琴, 姚乐野. 基于情景相似度的突发事件情报感知实现方法[J]. 情报学报, 2019, 38(5):525-533.
[8] (Yang Feng, Zhang Yueqin, Yao Leye. The Method for Intelligence Awareness in an Emergency Based on Scenario Similarity[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(5):525-533.)
[9] 徐绪堪, 李一铭. 基于情景相似度的突发事件多粒度响应模型研究[J]. 情报科学, 2021, 39(2):18-23, 43.
[9] (Xu Xukan, Li Yiming. Research on Multi-Granularity Response Model of Emergency Based on Scene Similarity[J]. Information Science, 2021, 39(2):18-23,43.)
[10] 涂伟, 曹劲舟, 高琦丽, 等. 融合多源时空大数据感知城市动态[J]. 武汉大学学报(信息科学版), 2020, 45(12):1875-1883.
[10] (Tu Wei, Cao Jinzhou, Gao Qili, et al. Sensing Urban Dynamics by Fusing Multi-Sourced Spatiotemporal Big Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12):1875-1883.)
[11] 黄晓斌, 张明鑫. 融合多源数据的企业竞争对手画像构建[J]. 现代情报, 2020, 40(11):13-21, 33.
[11] (Huang Xiaobin, Zhang Mingxin. Construction of Enterprise Competitor Portrait Based on Multi-Source Data[J]. Journal of Modern Information, 2020, 40(11):13-21, 33.)
[12] 郑荣, 杨竞雄, 张薇, 等. 多源数据驱动的产业竞争情报智慧服务研究[J]. 情报学报, 2020, 39(12):1295-1304.
[12] (Zheng Rong, Yang Jingxiong, Zhang Wei, et al. Research on Intelligent Services for Industrial Competitive Intelligence Driven by Multi-Source Data[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(12):1295-1304.)
[13] 张永和, 肖广德, 胡永斌, 等. 智慧学习环境中的学习情景识别——让学习环境有效服务学习者[J]. 开放教育研究, 2012, 18(1):85-89.
[13] (Zhang Yonghe, Xiao Guangde, Hu Yongbin, et al. An Approach to Recognize Learning Scenario in Smart Environment[J]. Open Education Research, 2012, 18(1):85-89.)
[14] Paula A R M, Mauricio G, Valentina T, et al. Educational Resources Recommendation System for a Heterogeneous Student Group[J]. Advances in Distributed Computing and Artificial Intelligence Journal, 2016, 5(3):21-30.
[15] Ahn Y, Kim Y. Semantic Cloud Resource Recommendation Using Cluster Analysis in Hybrid Cloud Computing Environment[J]. KIPS Transactions on Computer and Communication Systems, 2015, 4(9):283-288.
doi: 10.3745/KTCCS.2015.4.9.283
[16] Corinna J L, Freimut B. Analyzing Industry Stakeholders Using Open-source Competitive Intelligence - A Case Study in the Automotive Supply Industry[J]. Journal of Enterprise Information Management, 2020, 33(3):579-599.
doi: 10.1108/JEIM-08-2019-0234
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