Name
Abstract | ||
|---|---|---|
Modelling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items, while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural recommendation (NeuRec) model that untangles the complexity of user-item interactions and establish an integrated network to link a non-linear neural transformation part and latent factor part. To explore its capability, we design two variants of NeuRec: user based NeuRec (U-NeuRec) and item based NeuRec (I-NeuRec). Extensive experiments on four real-world datasets demonstrated its superior performances on personalized ranking task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.24963/ijcai.2018/510 | IJCAI |
DocType | Volume | Citations |
Conference | abs/1805.03002 | 10 |
PageRank | References | Authors |
0.45 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Zhang | 1 | 179 | 8.35 |
| Lina Yao | 2 | 981 | 93.63 |
| Aixin Sun | 3 | 3071 | 156.89 |
| Sen Wang | 4 | 477 | 37.24 |
| Guodong Long | 5 | 655 | 47.27 |
| Manqing Dong | 6 | 31 | 5.24 |