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Deep neural networks meet recommender systems

Deep neural networks meet recommender systems

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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
  • Multilayer perceptron
  • Autoencoder
  • Convolutional neural network
  • Recurrent neural network
  • 2.2 Introducing nonlinearity to recommender systems
  • 2.2.1 Deep neural generalization of collaborative filtering
  • Neural matrix factorization
  • 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

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