Name
Abstract | ||
|---|---|---|
Recently, in the area of big data, some popular applications such as web search engines and recommendation systems, face the problem to diversify results during query processing. In this sense, it is both significant and essential to propose methods to deal with big data in order to increase the diversity of the result set. In this paper, we firstly define the diversity of a set and the ability of an element to improve the overall diversity. Based on these definitions, we propose a diversification framework which has good performance in terms of effectiveness and efficiency. Also, this framework has theoretical guarantee on probability of success. Secondly, we design implementation algorithms based on this framework for both numerical and string data. Thirdly, for numerical and string data respectively, we carry out extensive experiments on real data to verify the performance of our proposed framework, and also perform scalability experiments on synthetic data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11704-019-8324-9 | Frontiers of Computer Science |
Keywords | Field | DocType |
diversification, query processing, big data | Recommender system,Search engine,Result set,Computer science,String Data.,Synthetic data,Diversification (marketing strategy),Artificial intelligence,Big data,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
14 | 4 | 2095-2228 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meifan Zhang | 1 | 0 | 1.69 |
| Hongzhi Wang | 2 | 421 | 73.72 |
| Jianzhong Li | 3 | 63 | 24.23 |
| Hong Gao | 4 | 1086 | 120.07 |