| 论文简介 |
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 |
(创新港)
