Self-study course: approximate and scalable inference for complex probabilistic models in recommender systems. part 1: models and representations session 2: - adding trust/side information - Bayesian extensions - poisson models (with trust and social network information) - Latent Gaussian Models

1. Part 1: Models and Representations
Session 1:
- Probabilistic Matrix Factorization
- Poisson Models (CF and Content-based)
Session 2:
- Adding trust/side information
- Bayesian extensions
- Poisson Models (trust/social)
- LGM (intro)
Session 3:
- State-space models
- Tensor-Factorization
- Time-dependent
Approximate and Scalable Inference for Complex
Probabilistic Models in Recommender Systems

2. Constrained PMF

3. Constrained PMF

4. SocialMF

5. SocialMF

6. SocialMF
● Inference

7. Bayesian PMF
- Adding (conjugate) priors to the
parameters

8. Bayesian PMF

9. Models with Residuals

10. Models with Residuals

11. Bayesian CPMF

12. Kernelized PMF

13. Kernelized PMF

14. Kernelized PMF

15. Social Poisson Factorization

16. Latent Gaussian Models