Name: Deep Learning for News Recommender Systems (en Inglés)
Price: 228.24 PEN
Availability: InStock
Author: Moreira, Gabriel ; Cunha, Adilson
ISBN: 9786202552219

portada Deep Learning for News Recommender Systems (en Inglés)

Formato

Libro Físico

Autor

Gabriel Moreira

Adilson Cunha

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

188

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 1.1 cm

Peso

0.28 kg.

ISBN13

9786202552219

Categorías

Tecnología: cuestiones generales

Deep Learning for News Recommender Systems (en Inglés)

Gabriel Moreira (Autor) · Adilson Cunha (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo

S/ 228,24

S/ 456,48

Ahorras: S/ 228,24

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 22 de Julio y el Miércoles 31 de Julio.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Deep Learning for News Recommender Systems (en Inglés)"

Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.