Name
Title | ||
|---|---|---|
Learning binary codes with neural collaborative filtering for efficient recommendation systems. |
Abstract | ||
|---|---|---|
The fast-growing e-commerce scenario brings new challenges to traditional collaborative filtering because the huge amount of users and items requires large storage and efficient recommendation systems. Hence, hashing for collaborative filtering has attracted increasing attention as binary codes can significantly reduce the storage requirement and make similarity calculations efficient. In this paper, we investigate the novel problem of deep collaborative hashing codes on user–item ratings. We propose a new deep learning framework for it, which adopts neural networks to better learn both user and item representations and make these close to binary codes such that the quantization loss is minimized. In addition, we extend the proposed framework for out-of-sample cases, i.e., dealing with new users, new items, and new ratings. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.knosys.2019.02.012 | Knowledge-Based Systems |
Keywords | Field | DocType |
Recommendation systems,Binary code learning,Neural networks,Neural collaborative hashing | Recommender system,Data mining,Collaborative filtering,Computer science,Binary code,Artificial intelligence,Hash function,Deep learning,Quantization (signal processing),Artificial neural network,Machine learning | Journal |
Volume | ISSN | Citations |
172 | 0950-7051 | 10 |
PageRank | References | Authors |
0.50 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 659 | 125.00 |
| Suhang Wang | 2 | 859 | 51.38 |
| Quan Pan | 3 | 568 | 47.06 |
| Haiyun Peng | 4 | 167 | 6.65 |
| Tao Yang | 5 | 10 | 1.85 |
| Erik Cambria | 6 | 3873 | 183.70 |