http://iet.metastore.ingenta.com
1887

Deep neural networks meet recommender systems

Deep neural networks meet recommender systems

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Big Data Recommender Systems - Volume 2: Application Paradigms — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Deep learning has been widely used in many software disciplines in both academia and industry including computer vision, speech recognition and translation, natural languages processing, search engine, bioinformatics, sensor data processing, finance, etc., due to its scalability in big data environments and accuracy at higher level than ever before. Especially, deep neural networks can utilize the parallel computational power of GPU to accelerate the learning process and ensure higher efficiency for big data problems.

Chapter Contents:

  • 2.1 Preliminary
  • 2.1.1 Introduction to recommender systems
  • 2.1.2 Introduction to deep neural networks
  • 2.1.2.1 Multilayer perceptron
  • 2.1.2.2 Autoencoder
  • 2.1.2.3 Convolutional neural network
  • 2.1.2.4 Recurrent neural network
  • 2.2 Introducing nonlinearity to recommender systems
  • 2.2.1 Deep neural generalization of collaborative filtering
  • 2.2.1.1 Neural matrix factorization
  • 2.2.1.2 Autoencoder-based collaborative filtering
  • 2.2.2 Deep neural generalization of factorization machine
  • 2.3 Representation learning for recommender systems
  • 2.3.1 Representation learning with multilayer perceptron
  • 2.3.2 Representation learning with autoencoder
  • 2.3.3 Representation learning with convolutional neural network
  • 2.3.4 Representation learning withWord2Vec
  • 2.4 Sequence modelling for recommender systems
  • 2.4.1 Session-based recommendations
  • 2.4.2 Sequence-aware recommender systems
  • 2.5 Deep hybrid models for recommender systems
  • 2.6 Advanced topics
  • 2.6.1 Metric learning
  • 2.6.2 Generative adversarial networks
  • 2.6.3 Neural autoregressive distribution estimator
  • 2.7 Future challenges and conclusion
  • References

Inspec keywords: Big Data; graphics processing units; recommender systems; neural nets; learning (artificial intelligence); parallel processing

Other keywords: parallel computation; Big Data; computational power; GPU; deep learning

Subjects: Microprocessor chips; Data handling techniques; Search engines; Knowledge engineering techniques; Neural computing techniques; Multiprocessing systems; Information networks

Preview this chapter:
Zoom in
Zoomout

Deep neural networks meet recommender systems, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch2-1.gif /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch2-2.gif

Related content

content/books/10.1049/pbpc035g_ch2
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address