Name
Abstract | ||
|---|---|---|
The number of website is increasing speedily, and clients purchase their website from the enterprise that suggests them the best domain name with a good price. In order to give the relevant domain name, enterprise is always eager to have a good system of suggestion that suits the client request. Recommender system has been an effective key solution to guide users in a personalized way for discovering the domain name they might be interested in from a large space of possible suggestion. They have become fundamental applications that provides to users the best domain name that meet their needs and preferences. In this work, we used a recommender system based on neural network as it is capable to solve many complex tasks and gives better customer satisfaction. We proposed two approaches to recommend domain name, both of these approaches are based on neural network. The first one consists on discovering the similarity between the vocabulary of domain name, while the second one is finding the relevant Top Level Domain (TLD) corresponding to the context of the domain name. First experiments on GANDI datasets shows the effectiveness of the proposed approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.10.505 | Procedia Computer Science |
Keywords | Field | DocType |
Text Mining,Neural Network,Natural Language Processing | Recommender system,Top level domain,Customer satisfaction,Domain name,Information retrieval,Computer science,Artificial intelligence,Artificial neural network,Vocabulary,Machine learning | Conference |
Volume | ISSN | Citations |
144 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaoutar Benlamine | 1 | 0 | 1.35 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Younès Bennani | 3 | 269 | 53.18 |
| Nicoleta Rogovschi | 4 | 40 | 8.42 |
| Kamel Haddadou | 5 | 62 | 10.34 |
| Ahmed Amamou | 6 | 26 | 3.62 |