Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (12): 1933-1940.doi: 10.23940/ijpe.20.12.p9.19331940

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Video Recommendation Algorithm based on Knowledge Graph and Collaborative Filtering

Di Yu*, Ruyun Chen, Juan Chen   

  1. School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, 524088, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address: d_13125037531@163.com
  • About author:
    Di Yu is a graduate student in the School of Mathematics and Computer at Guangdong Ocean University. Her research interests include recommendation algorithms, big data, and information analysis.
    Ruyun Chen is an professor in the School of Information Technology and Management at Guangdong Ocean University. His research interests include high-precision numerical algorithms and big data.
    Juan Chen is a graduate student in the School of Mathematics and Computer at Guangdong Ocean University. Her research interests include big data and information analysis.
  • Supported by:
    This work is supported by the NSF of Guangdong Province, China (Grant No. 2015A030313615).

Abstract: Traditional collaborative filtering algorithms make recommendations based on user behavior without considering semantic relationships between objects. Based on the normalization of the item similarity matrix according to the maximum value, this paper normalizes the user similarity matrix. That is, while reducing the influence of popular items on the recommended results, it also reduces the impact of active users. This paper uses the knowledge graph between items as auxiliary information, makes recommendations based on the confidence of the multi-path relationship, and merges the recommendation results with the user- and item-based collaborative filtering recommendation results. The processing method in this paper makes up for the defects of traditional collaborative filtering algorithms, which do not sufficiently consider hidden information, and it has higher accuracy, coverage, and recall.

Key words: knowledge graph, collaborative filtering, video recommendation, TopN