A group-oriented recommendation algorithm based on similarities of personal learning generative networks

• Date:2025-04-30
• Title of Paper:A group-oriented recommendation algorithm based on similarities of personal learning generative networks
• Journal:IEEE Access
• Summary:To solve the lack of consideration of the learning time sequence and knowledge dependencies
  in group-based recommendation, we proposed a novel group-oriented recommendation algorithm which is
  characterized by mapping the user’s learning log to a personal learning generative network (PLGN) based on
  a knowledge map. In this paper, we first provide calculation methods of similarity and temporal correlation
  between knowledge points, where we provide the construction method of the PLGN. Second, a method for
  measuring the similarities between any two PLGNs is proposed. According to the similarities, we perform
  the CURE clustering algorithm to obtain learning groups. Third, based on the group clustering, the group
  learning generative network using a graph overlay method is generated. We calculate the importance of the
  vertices on the different learning needs and propose a group-oriented recommendation algorithm. Finally,
  we compare the effect of the proposed recommendation to that of a group-based collaborative filtering
  recommendation for the aspects of precision rate, recall rate, normalized discounted cumulative gain, and
  the average accuracy of parameters (MAP). The experimental results show that the group-oriented learning
  recommendation based on the learning generated network outperforms the group recommendation-based
  collaborative filtering when the amount of data is large enough.
  
  https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8412180&tag=1
• Co-author:朱海萍、倪逸夫、田锋（通讯作者）等
• Translation or Not:No
• Date of Publication:2018-07-17