Name
Abstract | ||
|---|---|---|
With the rapid development of network technology, Internet has become an important tool to publish, exchange and acquire information. Many fields such as news, advertising, consuming, finance, education, E-commerce are involved. However, the huge, dynamic, heterogeneous and semi-structured data structure environment makes general search engine hard to avoid "topic drift", which needs users to choose the topic they are interested in. So, a topic specific search engine, which is more explicit in classification, having more data related to the topic and updates more timely is needed. To compensate for the general search engine's weakness processing domain information, this paper proposes a novel ranking algorithm based on machine learning for topic specific web pages, describes an experimental search engine based on this algorithm, and presents the experiment results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-484-8-430 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Topical specific,Search Engine,Multi-facet,Machine learning,SVM | Web page,Information retrieval,Ranking,Ranking SVM,Computer science,Support vector machine,Facet (geometry) | Conference |
Volume | ISSN | Citations |
274 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanjun Cao | 1 | 1 | 2.04 |
| Jin Liu | 2 | 316 | 50.24 |
| Jingtai Zhang | 3 | 0 | 0.34 |
| Fei Li | 4 | 97 | 39.93 |
| Bei Zhong | 5 | 0 | 1.01 |