![10
DSCF
Deep Social Collaborative Filtering, RecSys, 2019
Collaborative Filtering with users’ social relations
(Social Recommendation)
β1
β3
β4
β2
Output Layer
. . .
Embedding Layer
Random Walk Layer
α1
α2
α3
α4
1
h
2
h L
h
L
h
1
-
L
h
2
h 1
-
L
h
1
h
Sequence
Learning Layer
. . .
. . .
. . .
. . .
Attention Network
Attention Network
u[1] u[2] u[l]
r'
…
…
Item Embedding
User Embedding
Rating Embedding
Concatenation
Concatenation
… …
…
Users might be affected by direct/distant neighbors.
Ø Information diffusion
Ø Users with high reputations
News
US could see millions of coronavirus
cases and 100,000 or more deaths
Dr. Anthony Fauci](https://image.slidesharecdn.com/recsysijcaidnn-210823024923/85/Fundamentals-of-Deep-Recommender-Systems-10-320.jpg)
![11
DSCF
Deep Social Collaborative Filtering, RecSys, 2019
Collaborative Filtering with users’ social relations
(Social Recommendation)
β1
β3
β4
β2
Output Layer
. . .
Embedding Layer
Random Walk Layer
α1
α2
α3
α4
1
h
2
h L
h
L
h
1
-
L
h
2
h 1
-
L
h
1
h
Sequence
Learning Layer
. . .
. . .
. . .
. . .
Attention Network
Attention Network
u[1] u[2] u[l]
r'
…
…
Item Embedding
User Embedding
Rating Embedding
Concatenation
Concatenation
… …
…
News
US could see millions of coronavirus
cases and 100,000 or more deaths
Dr. Anthony Fauci
Users might be affected by direct/distant neighbors.
Ø Information diffusion
Ø Users with high reputations
Social Sequences
via Random Walk
techniques
Bi-LSTM with
attention
mechanisms](https://image.slidesharecdn.com/recsysijcaidnn-210823024923/85/Fundamentals-of-Deep-Recommender-Systems-11-320.jpg)
![16
Optimization for Ranking Tasks
p Negative Sampling’s Main Issue:
• It often generates low-quality negative samples that do not help you learn good
representation [Cai and Wang, 2018; Wang et al., 2018b].
Dress
Male
Men’s Wallets
Female
informative
‘difficult’
“easy”/unrelated “easy”/unrelated
Deep Adversarial Social Recommendation, IJCAI, 2019](https://image.slidesharecdn.com/recsysijcaidnn-210823024923/85/Fundamentals-of-Deep-Recommender-Systems-16-320.jpg)
![17
Optimization for Ranking Tasks
p Negative Sampling’s Main Issue:
• It often generates low-quality negative samples that do not help you learn good
representation [Cai and Wang, 2018; Wang et al., 2018b].
Dress
Male
Men’s Wallets
Female
informative
‘difficult’
“easy”/unrelated “easy”/unrelated
Dynamically generate
“difficult” negative samples
Optimization with
Adversarial Learning
(GAN)
Deep Adversarial Social Recommendation, IJCAI, 2019](https://image.slidesharecdn.com/recsysijcaidnn-210823024923/85/Fundamentals-of-Deep-Recommender-Systems-17-320.jpg)
Fundamentals of Deep Recommender Systems
- 1. Fundamentals of Deep Recommender Systems 1 Data Science and Engineering Lab Tutorial website: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/ Wenqi Fan The Hong Kong Polytechnic University https://wenqifan03.github.io, wenqifan@polyu.edu.hk
- 2. 2 A General Architecture of Deep Recommender System Embedding layer Prediction layer 0 0 1 Field 1 Field m Field M 0 1 0 1 0 0 User Item Context Interaction Hidden layers (e.g., MLP, CNN, RNN, etc. )
- 3. 3 NeuMF Neural Collaborative Filtering, WWW, 2017 NeuMF unifies the strengths of MF and MLP in modeling user-item interactions. p MF uses an inner product as the interaction function p MLP is more sufficient to capture the complex structure of user interaction data
- 4. 4 Wide&Deep Wide & Deep Learning for Recommender Systems, 1st DLRS, 2016 Standard MLP Embedding Concatenation p The wide linear models can memorize seen feature interactions using cross-product feature transformations. p The deep models can generalize to previously unseen feature interactions through low- dimensional embeddings.
- 5. 5 Neural FM Neural Factorization Machines for Sparse Predictive Analytics, SIGIR, 2017 Neural Factorization Machines (NFMs) “deepens” FM by placing hidden layers above second-order feature interaction modeling. categorical variables
- 6. 6 Neural FM Neural Factorization Machines for Sparse Predictive Analytics, SIGIR, 2017 Neural Factorization Machines (NFMs) “deepens” FM by placing hidden layers above second-order feature interaction modeling. “Deep layers” learn higher-order feature interactions only, being much easier to train. Bilinear Interaction Pooling: 𝑓!" 𝑉 # = $ $%& ' $ (%$)& ' 𝑥$ 𝐯$ ⨀𝑥(v( categorical variables
- 7. 7 DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, IJCAI, 2017 DeepFM ensembles FM and DNN and to low- and high-order feature interactions simultaneously from the input raw features. Prediction Model: FM component (low-order) Deep component (high-order)
- 8. 8 DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, IJCAI, 2017 DeepFM ensembles FM and DNN and to low- and high-order feature interactions simultaneously from the input raw features. FM component (low-order) Deep component (high-order) Share the embedding layer Prediction Model:
- 9. 9 DSCF Deep Social Collaborative Filtering, RecSys, 2019 Collaborative Filtering with users’ social relations (Social Recommendation) News US could see millions of coronavirus cases and 100,000 or more deaths Dr. Anthony Fauci
- 10. 10 DSCF Deep Social Collaborative Filtering, RecSys, 2019 Collaborative Filtering with users’ social relations (Social Recommendation) β1 β3 β4 β2 Output Layer . . . Embedding Layer Random Walk Layer α1 α2 α3 α4 1 h 2 h L h L h 1 - L h 2 h 1 - L h 1 h Sequence Learning Layer . . . . . . . . . . . . Attention Network Attention Network u[1] u[2] u[l] r' … … Item Embedding User Embedding Rating Embedding Concatenation Concatenation … … … Users might be affected by direct/distant neighbors. Ø Information diffusion Ø Users with high reputations News US could see millions of coronavirus cases and 100,000 or more deaths Dr. Anthony Fauci
- 11. 11 DSCF Deep Social Collaborative Filtering, RecSys, 2019 Collaborative Filtering with users’ social relations (Social Recommendation) β1 β3 β4 β2 Output Layer . . . Embedding Layer Random Walk Layer α1 α2 α3 α4 1 h 2 h L h L h 1 - L h 2 h 1 - L h 1 h Sequence Learning Layer . . . . . . . . . . . . Attention Network Attention Network u[1] u[2] u[l] r' … … Item Embedding User Embedding Rating Embedding Concatenation Concatenation … … … News US could see millions of coronavirus cases and 100,000 or more deaths Dr. Anthony Fauci Users might be affected by direct/distant neighbors. Ø Information diffusion Ø Users with high reputations Social Sequences via Random Walk techniques Bi-LSTM with attention mechanisms
- 12. 12 DASO Deep Adversarial Social Recommendation, IJCAI, 2019 3 5 1 Item Domain Social Domain p User behave and interact differently in the item/social domains. Collaborative Filtering with users’ social relations (Social Recommendation)
- 13. 13 DASO Deep Adversarial Social Recommendation, IJCAI, 2019 3 5 1 Item Domain Social Domain p User behave and interact differently in the item/social domains. Learning separated user representations in two domains. Collaborative Filtering with users’ social relations (Social Recommendation)
- 14. 14 DASO Deep Adversarial Social Recommendation, IJCAI, 2019 3 5 1 Item Domain Social Domain p User behave and interact differently in the item/social domains. Learning separated user representations in two domains. Bidirectional Knowledge Transfer with Cycle Reconstruction Collaborative Filtering with users’ social relations (Social Recommendation)
- 15. 15 Optimization for Ranking Tasks p Negative Sampling’s Main Issue: • It often generates low-quality negative samples that do not help you learn good representation. Deep Adversarial Social Recommendation, IJCAI, 2019
- 16. 16 Optimization for Ranking Tasks p Negative Sampling’s Main Issue: • It often generates low-quality negative samples that do not help you learn good representation [Cai and Wang, 2018; Wang et al., 2018b]. Dress Male Men’s Wallets Female informative ‘difficult’ “easy”/unrelated “easy”/unrelated Deep Adversarial Social Recommendation, IJCAI, 2019
- 17. 17 Optimization for Ranking Tasks p Negative Sampling’s Main Issue: • It often generates low-quality negative samples that do not help you learn good representation [Cai and Wang, 2018; Wang et al., 2018b]. Dress Male Men’s Wallets Female informative ‘difficult’ “easy”/unrelated “easy”/unrelated Dynamically generate “difficult” negative samples Optimization with Adversarial Learning (GAN) Deep Adversarial Social Recommendation, IJCAI, 2019
- 18. p(v|u) p(uk|u) Loss/Reward Generator Discriminator Discriminator Generator Reward Reward Generated Samples Generated Samples Cyclic User Modeling Item Domain Adversarial Learning UI UIS USI US hI->S hS->I Loss/Reward Real Samples User-Item Interactions User-User Connections Real Samples Social Domain Representations for Generator Social Domain Representations for Discriminator Social Domain Adversarial Learning V U u v U Uk U Uk User Representations on Social Domain after Mapping (I->S) Item Domain Representations for Generator User Representations on Item Domain after Mapping (S->I) Item Domain Representations for Discriminator 18 DASO Deep Adversarial Social Recommendation, IJCAI, 2019
- 19. p(v|u) p(uk|u) Loss/Reward Generator Discriminator Discriminator Generator Reward Reward Generated Samples Generated Samples Cyclic User Modeling Item Domain Adversarial Learning UI UIS USI US hI->S hS->I Loss/Reward Real Samples User-Item Interactions User-User Connections Real Samples Social Domain Representations for Generator Social Domain Representations for Discriminator Social Domain Adversarial Learning V U u v U Uk U Uk User Representations on Social Domain after Mapping (I->S) Item Domain Representations for Generator User Representations on Item Domain after Mapping (S->I) Item Domain Representations for Discriminator 19 DASO Deep Adversarial Social Recommendation, IJCAI, 2019
- 20. 20 Item Domain Discriminator Model p Discriminator Score function: Goal: distinguish real user-item pairs (i.e., real samples) and the generated “fake” samples (relevant) (Sigmoid) Deep Adversarial Social Recommendation, IJCAI, 2019
- 21. 21 Item Domain Generator Model p Generator Model Goal: 1. Approximate the underlying real conditional distribution pI real(v|ui) 2. Generate (select/sample) the most relevant items for any given user ui. Optimization with Policy Gradient the transferred user representation from social domain
- 22. 22 Sequential (Session-based) Recommendation Session-based Recommendations with Recurrent Neural Networks, ICLR, 2016. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM, 2019. user’s sequential behavior Next Item 0.8 0.6 0.1
- 23. 23 Sequential (Session-based) Recommendation Session-based Recommendations with Recurrent Neural Networks, ICLR, 2016. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM, 2019. GRU based sequential recommendation method (GRU4Rec) user’s sequential behavior Next Item 0.8 0.6 0.1
- 24. 24 Sequential (Session-based) Recommendation Session-based Recommendations with Recurrent Neural Networks, ICLR, 2016. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM, 2019. GRU based sequential recommendation method (GRU4Rec) user’s sequential behavior Next Item 0.8 0.6 0.1 BERT4Rec Transformer Layer
- 25. 25 Recommendation Policies § Offline optimization § Short-term reward Shortcomings of Existing Deep Recommender Systems
- 26. 26 Recommendation Policies § Offline optimization § Short-term reward Graph-structured Data § Information isolated island Issue: ignore implicit/explicit relationships among instances Shortcomings of Existing Deep Recommender Systems Items Users !! "! !! "" !! "# !" "! … …
- 27. 27 Recommendation Policies § Offline optimization § Short-term reward 0 0 1 Field 1 Field m Field M 0 1 0 1 0 0 Manually Deisgned Architectures § Expert knowledge § Time and engineering efforts Graph-structured Data § Information isolated island Issue: ignore implicit/explicit relationships among instances Shortcomings of Existing Deep Recommender Systems Items Users !! "! !! "" !! "# !" "! … …
- 28. 28 Recommendation Policies § Offline optimization § Short-term reward 0 0 1 Field 1 Field m Field M 0 1 0 1 0 0 Manually Deisgned Architectures § Expert knowledge § Time and engineering efforts Graph-structured Data § Information isolated island Issue: ignore implicit/explicit relationships among instances Shortcomings of Existing Deep Recommender Systems Items Users !! "! !! "" !! "# !" "! … … ... ... ... ... ... ... ... ... ... ... Normal Data Fake User Profiles Generating Fake User Profiles Attacker promote/demote Poisoning attacks: § Promote/demote items § White/grey/black-box attacks