Name
Abstract | ||
|---|---|---|
In order to accurately obtain potential features and improve the recommendation performance of the collaborative filtering algorithm, this paper puts forward a collaborative filtering recommendation algorithm based on deep neural network fusion (CF-DNNF). CF-DNNF makes the best of the implicit attributes of data, where the text attributes and the other attributes are extracted from the data through the long short-term memory (LSTM) network and the deep neural network, respectively, so as to obtain the feature matrix that contains the user and item attribute information. Deep belief network (DBN) uses the feature matrix and outputs the probability. Besides, this paper initially discusses an interpretable collaborative filtering recommendation algorithm based on deep neural network fusion (ICF-DNNF). The paper compares the CF-DNNF algorithm with probabilistic matrix factorisation (PMF), SVD, and restricted Boltzmann-based collaborative filtering (RBM-CF) algorithms on the MovieLens dataset and the Amazon product dataset. Results indicate that the root mean square error (RMSE) of CF-DNNF is improved by 2.015%, and the mean absolute error (MAE) is improved by 2.222%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1504/IJSNET.2020.110460 | INTERNATIONAL JOURNAL OF SENSOR NETWORKS |
Keywords | DocType | Volume |
recommendation, algorithm, feature, interpretable, fusion, neural network, collaborative filtering, deep learning, MovieLens, RBM, restricted Boltzmann machine, CF-DNNF | Journal | 34 |
Issue | ISSN | Citations |
2 | 1748-1279 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Juan | 1 | 9 | 4.63 |
| Baocai Li | 2 | 0 | 0.34 |
| Mingxia Gao | 3 | 0 | 0.34 |