Research on Semi-supervised Recommendation Algorithm Based on Hybrid Model
Zhi-Hong Nan, Fei Zhao · 2020
In the algorithm model training, the scale of the labeled data and the type of algorithm model determines the accuracy of algorithm model. In reality, when training a new system, the amount of unlabeled data is often much larger than that of labeled data. There are linear models, matrix decomposition models and so on for the selection of recommended algorithm models. Given the above problems, consider using the semi-supervised co-training method to construct a dual-view hybrid model algorithm framework through co-training of recommendation algorithm models based on different mathematical foundations. The algorithm framework is verified on the movie data set MovieLens. The experimental results show that the semi-supervised co-training recommendation algorithm based on the hybrid model reduces the root-mean-square error by 1.4%, the accuracy of recommendation results is improved.