Research on MOOC Recommendation Method Based on LDA Topic Model
With the increasing number of MOOC platforms and the explosive growth of course resources,it is difficult for us-ers to quickly find the courses they are interested in a relatively short period of time.Aiming at the above problems,a collaborative filtering algorithm based on LDA classification and dimension reduction is used to provide users with personalized course recommen-dations.First,the multinomial distribution of the LDA topic model is estimated by using Gibbs sampling method to cluster the MOOC course text information.Secondly,a category-scoring matrix is constructed according to the clustering results of the course information combined with the user behavior set.Finally,according to the category-rating matrix information,the user cosine simi-larity is calculated,and the target user is recommended for collaborative filtering.The experimental results show that the accuracy,recall rate,comprehensive evaluation index F1 value and prediction score accuracy of the collaborative filtering algorithm based on LDA classification and dimension reduction are improved compared with the traditional recommendation algorithm.
MOOC platformpersonalized recommendation of coursesLDA topic modelclusteringcollaborative filtering
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