Name
Abstract | ||
|---|---|---|
In recent years, deep neural networks have been widely applied to recommender systems. Although there are extensive explorations of deep neural networks on the collaborative filtering problem in item recommendation, most of the existing methods employ a similar loss function, i.e., the prediction loss of user-item interactions, and only change the form of the input, which may limit the model's performance. To address this problem, we present a novel framework, named DCAR, short for Deep Collaborative Autoencoder for Recommendation. Specifically, with the implicit feedback matrix as the input, we employ the autoencoder module to obtain the latent representations of users and items respectively. Then, to predict the matching score of corresponding user-item pairs, an interaction prediction module is designed based on the neural network architecture. The two parts are coupled together and employ alternating training to learn. We conduct extensive experiments on several real-world datasets and the results empirically verify the superior performance of DCAR on item recommendation. The code related to this paper is available at: https://github.com/strange-jiong/DCAR. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30490-4_15 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: TEXT AND TIME SERIES, PT IV |
Keywords | DocType | Volume |
User-item autoencoder, Collaborative filtering, Matching function learning, Recommender system | Conference | 11730 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiong Wang | 1 | 49 | 12.67 |
| Neng Gao | 2 | 1 | 6.44 |
| Peng Jia | 3 | 87 | 23.41 |
| Jingjie Mo | 4 | 0 | 0.34 |