Name
Abstract | ||
|---|---|---|
In recent years, recommendation systems have been widely used in various commercial platforms to provide recommendations for users. Collaborative filtering algorithms are one of the main algorithms used in recommendation systems. Such algorithms are simple and efficient; however, the sparsity of the data and the scalability of the method limit the performance of these algorithms, and it is difficult to further improve the quality of the recommendation results. Therefore, a model combining a collaborative filtering recommendation algorithm with deep learning technology is proposed, therein consisting of two parts. First, the model uses a feature representation method based on a quadric polynomial regression model, which obtains the latent features more accurately by improving upon the traditional matrix factorization algorithm. Then, these latent features are regarded as the input data of the deep neural network model, which is the second part of the proposed model and is used to predict the rating scores. Finally, by comparing with other recommendation algorithms on three public datasets, it is verified that the recommendation performance can be effectively improved by our model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2789866 | IEEE ACCESS |
Keywords | Field | DocType |
Recommendation system,collaborative filtering,quadric polynomial regression,deep neural network (DNN) | Recommender system,Data mining,Data modeling,Algorithm design,Collaborative filtering,Computer science,Matrix decomposition,Artificial intelligence,Deep learning,Artificial neural network,Scalability,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 6 |
PageRank | References | Authors |
0.40 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Libo Zhang | 1 | 36 | 13.37 |
| Tiejian Luo | 2 | 120 | 26.96 |
| zhang fei | 3 | 24 | 7.85 |
| Yanjun Wu | 4 | 73 | 23.02 |