Recommendation Systems Lecture
Network theory has very many application areas, where graphs are of primary importance, in e.g.,:
-Communication networks
-Epidemiology
-Systems Biology
-Social networks.
Social Media (e.g., Twitter, Facebook, Instagram, to name a few) has had a tremendous growth in the past 20 years. Social Media Analysis has very many applications, e.g.,:
-Recommendation Systems
-Sentiment Analysis
-Information Diffusion
-Web Search.
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.