Name
Abstract | ||
|---|---|---|
Cold-start is a long-standing and challenging problem in recommendation systems. To tackle this issue, many cross-domain recommendation approaches are proposed. However, most of them follow a two-stage embedding-and-mapping paradigm, which is hard to be optimized. Besides, they ignore the structure information of the user-item interaction graph, resulting in that the embedding is insufficient to capture the latent collaborative filtering effect. In this paper, we propose a Dual Autoencoder Network (DAN), which implements cross-domain recommendations to cold-start users in an end-to-end manner. The graph convolutional network (GCN) based encoder in DAN explicitly captures high-order collaborative information in user-item interaction graphs. The two-branched decoder is proposed for fully exploiting the data across domains, and therefore the elaborate reconstruction constraints are obtained under a domain swapping strategy. Experiments on two pairs of real-world cross-domain datasets demonstrate that DAN outperforms existing state-of-the-art methods.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3412069 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6859-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bei wang | 1 | 2 | 4.83 |
| Chenrui Zhang | 2 | 14 | 3.34 |
| Hao Zhang | 3 | 207 | 58.59 |
| Xiaoqing Lyu | 4 | 3 | 3.75 |
| Zhi Tang | 5 | 256 | 53.42 |