Name
Abstract | ||
|---|---|---|
There has been an exponential growth in the volume and variety of information available on the Internet, similarly there has been a significant demand from users’ for accurate information that matches their interests, however, the two are often incompatible because of the effectiveness of retrieving the exact information the user requires. This paper addresses this problem with an adaptive agent-based modelling approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An adaptive information retrieval system is developed whose retrieval effectiveness is evaluated using traditional precision and recall. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s12652-012-0138-7 | J. Ambient Intelligence and Humanized Computing |
Keywords | DocType | Volume |
User information needs modelling, Interactive evolutionary learning, Adaptive information retrieval | Journal | 5 |
Issue | ISSN | Citations |
1 | 1868-5145 | 9 |
PageRank | References | Authors |
0.49 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeedeh Maleki-dizaji | 1 | 9 | 0.83 |
| Jawed I. A. Siddiqi | 2 | 210 | 45.09 |
| Yasaman Soltan-Zadeh | 3 | 9 | 0.83 |
| Fazilatur Rahman | 4 | 12 | 4.61 |