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![Word2Vec
• [Mikolov et. al, 2013a]
• Representation learning of words
• Shallow model
• Data: (target) word + context pair...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-25-2048.jpg?cb=1666368605)
![Paragraph2vec, doc2vec
• [Le & Mikolov, 2014]
• Learns representation of
paragraph/document
• Based on CBOW model
• Parag...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-29-2048.jpg?cb=1666368605)
![Prod2Vec
• [Grbovic et. al, 2015]
• Skip-gram model on products
– Input: i-th product purchased by the user
– Context: the...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-31-2048.jpg?cb=1666368605)
![Prod2Vec
[Grbovic et. al, 2015]
pro2vec skip-gram model on products](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-32-2048.jpg?cb=1666368605)
![Bagged Prod2Vec
[Grbovic et. al, 2015]
bagged-prod2vec model updates](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-33-2048.jpg?cb=1666368605)
![User-Prod2Vec
[Grbovic et. al, 2015]
User embeddings for user to product predictions](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-34-2048.jpg?cb=1666368605)
![Utilizing more information
• Meta-Prod2vec [Vasile et. al, 2016]
– Based on the prod2vec model
– Uses item metadata
• Embe...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-35-2048.jpg?cb=1666368605)
![References
• [Barkan & Koenigstein, 2016] O. Barkan, N. Koenigstein: ITEM2VEC: Neural item embedding for
collaborative fi...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-36-2048.jpg?cb=1666368605)
![Autoencoders for recommendation
• Reconstruct corrupted user interaction vectors
– CDL [Wang et. al, 2015]
Collaborative D...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-42-2048.jpg?cb=1666368605)
![Autoencoders for recommendation
• Reconstruct corrupted user interaction vectors
– CDAE [Wu et. al, 2016]
Collaborative De...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-43-2048.jpg?cb=1666368605)
![Recurrent autoencoder
• CRAE [Wang et. al, 2016]
– Collaborative Recurrent Autoencoder
– Encodes text (e.g. movie plot, re...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-44-2048.jpg?cb=1666368605)
![DeepCF methods
• MV-DNN [Elkahky et. al, 2015]
– Multi-domain recommender
– Separate feedforward networks for user and ite...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-45-2048.jpg?cb=1666368605)
![DeepCF methods
• TDSSM [Song et. al, 2016]
• Temporal Deep Semantic Structured Model
• Similar to MV-DNN
• User features a...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-46-2048.jpg?cb=1666368605)
![DeepCF methods
• Coevolving features [Dai et. al, 2016]
• Users’ taste and items’ audiences change over time
• User/item f...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-47-2048.jpg?cb=1666368605)
![DeepCF methods
• Product Neural Network (PNN) [Qu et. al, 2016]
– For CTR estimation
– Embed features
– Pairwise layer: al...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-48-2048.jpg?cb=1666368605)
![Applications: app recommendations
• Wide & Deep Learning [Cheng et. al, 2016]
• Ranking of results matching a query
• Comb...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-49-2048.jpg?cb=1666368605)
![Applications: video recommendations
• YouTube Recommender [Covington et. al, 2016]
– Two networks
– Candidate generation
•...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-50-2048.jpg?cb=1666368605)
![References
• [Cheng et. al, 2016] HT. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrad...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-51-2048.jpg?cb=1666368605)
![Convolutional Neural Networks (CNN)
• Some examples
• GoogLeNet [Szegedy et. al, 2015]
• Inception-v3 model [Szegedy et. a...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-59-2048.jpg?cb=1666368605)
![Images in recommenders
• [McAuley et. Al, 2015]
– Learns a parameterized distance metric over visual
features
• Visual fe...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-60-2048.jpg?cb=1666368605)
![Images in recommenders
• Visual BPR [He & McAuley, 2016]
– Model composed of
• Bias terms
• MF model
• Visual part
– Pretr...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-61-2048.jpg?cb=1666368605)
![Music representations
• [Oord et. al, 2013]
– Extends iALS/WMF with audio
features
• To overcome cold-start
– Music featu...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-62-2048.jpg?cb=1666368605)
![Textual information improving
recommendations
• [Bansal et. al, 2016]
– Paper recommendation
– Item representation
• Text...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-63-2048.jpg?cb=1666368605)
![References
• [Bansal et. al, 2016] T. Bansal, D. Belanger, A. McCallum: Ask the GRU: Multi-Task
Learning for Deep Text Re...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-64-2048.jpg?cb=1666368605)
![Long-Short Term Memory (LSTM)
• [Hochreiter & Schmidhuber, 1999]
• Instead of rewriting the hidden state during update,
a...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-70-2048.jpg?cb=1666368605)
![Gated Recurrent Unit (GRU)
• [Cho et. al, 2014]
• Simplified information flow
– Single hidden state
– Input and forget gat...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-71-2048.jpg?cb=1666368605)
![GRU4Rec (1/3)
• [Hidasi et. al, 2015]
• Network structure
– Input: one hot encoded item ID
– Optional embedding layer
– GR...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-73-2048.jpg?cb=1666368605)
![Extensions
• Multi-modal information (p-RNN model) [Hidasi et. al, 2016]
– Use image and description besides the item ID
–...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-77-2048.jpg?cb=1666368605)
![References
• [Chatzis et. al, 2017] S. P. Chatzis, P. Christodoulou, A. Andreou: Recurrent Latent Variable Networks for Se...](https://image.slidesharecdn.com/dltutorialrecsys17final-170830154416/75/Deep-Learning-for-Recommender-Systems-RecSys2017-Tutorial-78-2048.jpg?cb=1666368605)
