Name
Abstract | ||
|---|---|---|
In recent years, with the rapidly development of Internet and pharmaceutical market, online medical platform has become a major place for online medical trading. Recommendation systems have been widely deployed in commercial platform to improve user experience and sales. Motivated by this, we propose two hybrid recommendation algorithms, CB-CF hybrid algorithm and CNN-based CF algorithm, for B2B medical platform to provide accurate recommendations. We also give a brief introduction of two well-known recommendation algorithms, content-based algorithm and model-based CF algorithm. Then we investigate the performance of recommendation algorithms on Apache Spark and Tensorflow with real-world data collected from a china B2B online medical platform. Experimental results show that the hybrid recommendation algorithm performs better than other algorithms. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Recommender system,User experience design,Collaborative filtering,Spark (mathematics),Hybrid algorithm,Computer science,Algorithm,Artificial intelligence,Deep learning,The Internet |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
17 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingyun Dai | 1 | 148 | 23.91 |
| XueBin Hong | 2 | 0 | 0.34 |
| Jun Cai | 3 | 1 | 1.03 |
| Yan Liu | 4 | 0 | 0.68 |
| Huimin Zhao | 5 | 206 | 23.43 |
| Jian-Zhen Luo | 6 | 0 | 0.34 |
| ZeYu Lin | 7 | 0 | 0.34 |
| ShiJian Chen | 8 | 0 | 0.34 |