This lecture overviews Recommendation Systems that has many applications in Web Science, Marketing and Social Media Analytics. It covers the following topics in detail: Content Based Filtering. Collaborative Filtering: Memory Based Techniques, Model Based Techniques, Hybrid Techniques. ΚΝΝ algorithm. ALS algorithm. Learning from Implicit Datasets. Matrix Factorization: Funk MF, SVD++, Asymmetric SVD. Hybridization techniques. Deep Learning in Recommender Systems: MLP, Deep Factorization Machine, Restricted Boltzman Machines, Neural Autoregressive Density Estimators (NADE). Evaluation of Recommender Systems. Netflix Challenge.
