1. Deep learning techniques such as convolutional neural networks, recurrent neural networks, and autoencoders can be applied to recommender systems.
2. Convolutional neural networks are commonly used to extract features from images, audio, and video that can then be used for recommendation. Recurrent neural networks can model user sessions as sequences of clicks.
3. Autoencoders learn lower-dimensional representations of items that capture similarities and can be used to make recommendations, especially for cold start problems where little is known about new users or items.
16. Backpropagation
Does not work well in plain a “normal”
multilayer deep network
Vanishing Gradients
Slow Learning
SVM’s easier to train
2nd Neural Winter
40. Convolutional Networks for
enhancing Collaborative Filtering
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback He,
etl AAAI 2015
41. Convolutional Networks for Music
feature extraction
Deep Learning can be used to learn item profiles e.g. music
Map audio to lower dimensional space where it can be used
directly for recommendation
Useful in recommending music from the long tail (not popular)
A solution to the cold start problem
42. Convolutional Networks for Music
feature extraction
A. van den Oord, S. Dielmann, B. Schrauwen Deep content-
based music recommendation NIPS 2014
47. Recurrent Neural Networks
PANDARUS:
Alas, I think he shall be come approached and the day
When little srain would be attain'd into being never fed,
And who is but a chain and subjects of his death,
I should not sleep.
Second Senator:
They are away this miseries, produced upon my soul,
Breaking and strongly should be buried, when I perish
The earth and thoughts of many states.
DUKE VINCENTIO:
Well, your wit is in the care of side and that.
Second Lord:
They would be ruled after this chamber, and
my fair nues begun out of the fact, to be conveyed,
Whose noble souls I'll have the heart of the wars.
Clown:
Come, sir, I will make did behold your worship.
VIOLA:
I'll drink it.
52. Session-based recommendation
with Recurrent Neural Networks
RNN (GRU) with ranking loss function
ICLR 2016 [B. Hidasi, et.al.]
Treat each user session as sequence of clicks
53. Session-based recommendation
with Recurrent Neural Networks
RNN (GRU) with ranking loss function
ICLR 2016 [B. Hidasi, et.al.]
Treat each user session as sequence of clicks
58. (Some) Deep Learning Software
Theano: Python Library
TensorFlow: Python Library
Keras: High Level Python Library (Theano &TF)
MXNET: R, Python, Julia
59. Thanks
● Some slides or parts of slides are taken from
other excellent talks and papers on Deep
Learning (e.g. Yan Lecun, Andrej Karpathy and
other great deep learning researchers)