The tutorial will be divided into following sessions:

1. Introduction: motivation and goal of RS
2. Memory-based Collaborative Filtering models
   a. Theory
   i. Item-Item approach
   ii. User-User approach
   iii. Neighbourhood and similarity selection methods
   iv. Train/Dev set splitting methods
   b. Implementation of the model on selected dataset
3. Model-based Collaborative Filtering models
   a. Theory
   i. Different approaches to matrix factorisation (ALS, SDG,…)
   ii. Quality metrics for model-based CF
   iii. Problems of CF (Sparsity, Cold Start, Gray Sheep, Neighbourhood transivity, …
   b. Implementation of the model on selected dataset
4. Implicit Ratings in Recommendation Systems
   a. Theory
   i. Difference in approach in explicit vs Implicit RS
   ii. Changes in the model in comparison to Model-based CF
   iii. Quality Metrics for Implicit Ratings
   b. Implementation of the model on selected dataset
5. Summary and conclusions

What is needed: Anaconda (5.0+), TensofFlow (Single CPU is fine) Practical Exercises will be provided in Jupyter.