Name
Abstract | ||
|---|---|---|
Interactive recommendation has drawn widespread attention from both academia and industry due to its effectiveness in real-world mobile applications. Instead of receiving message passively, customers can exploit further with less effort through generated queries. Usually, such systems mainly contain two main components: query generation and item recommendation. In this paper, we propose a novel fr... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MDM52706.2021.00030 | 2021 22nd IEEE International Conference on Mobile Data Management (MDM) |
Keywords | DocType | ISSN |
Recommender System,Query Generation,Information Retrieval,Transformer | Conference | 1551-6245 |
ISBN | Citations | PageRank |
978-1-6654-2845-3 | 0 | 0.34 |
References | Authors | |
10 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohao Cai | 1 | 73 | 3.61 |
| Xiaoguang Li | 2 | 0 | 0.68 |
| Quanyu Dai | 3 | 28 | 5.28 |
| Gang Wang | 4 | 0 | 0.34 |
| Zhenhua Dong | 5 | 91 | 9.03 |
| Chaoliang Zhang | 6 | 0 | 0.34 |
| Xiuqiang He | 7 | 312 | 39.21 |
| Lifeng Shang | 8 | 485 | 30.96 |