| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560423 |
| ISSN: | 1405-5546 |
| Autores: | Krishna, Shubham1 Bajaj, Ahsaas1 Rungta, Mukund1 Tiwari, Hemant1 Vala, Vanraj1 |
| Instituciones: | 1Samsung R&D Institute, Bengaluru. India |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 969-978 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Information Retrieval Systems have revolutionized the organization and extraction of Information. In recent years, mobile applications (apps) have become primary tools of collecting and disseminating information. However, limited research is available on how to retrieve and organize mobile apps on users' devices. In this paper, authors propose a novel method to estimate app-embeddings which are then applied to tasks like app clustering, classification, and retrieval. Usage of app-embedding for query expansion, nearest neighbor analysis enables unique and interesting use cases to enhance end-user experience with mobile apps. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Information systems and retrieval, Mobile applications, Application embedding, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
