Name
Abstract | ||
|---|---|---|
In this paper, we present a novel Integer Linear Programming formulation (termed ILP4ID) for implicit search result diversification (SRD). The advantage is that the exact solution can be achieved, which enables us to investigate to what extent using the greedy strategy affects the performance of implicit SRD. Specifically, a series of experiments are conducted to empirically compare the state-of-the-art methods with the proposed approach. The experimental results show that: (1) The factors, such as different initial runs and the number of input documents, greatly affect the performance of diversification models. (2) ILP4ID can achieve substantially improved performance over the state-of-the-art methods in terms of standard diversity metrics. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48051-0_29 | INFORMATION RETRIEVAL TECHNOLOGY, AIRS 2016 |
Keywords | Field | DocType |
Search result diversification, ILP, Optimization | Data mining,Mathematical optimization,Computer science,Integer linear programming formulation,Diversification (marketing strategy) | Conference |
Volume | ISSN | Citations |
9994 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai-Tao Yu | 1 | 0 | 1.01 |
| Adam Jatowt | 2 | 903 | 106.73 |
| Roi Blanco | 3 | 872 | 57.42 |
| Hideo Joho | 4 | 881 | 70.47 |
| Joemon M. Jose | 5 | 2782 | 198.37 |
| Long Chen | 6 | 88 | 6.15 |
| Fajie Yuan | 7 | 143 | 14.55 |