CS361 (Software Engineering Program) Artificial Intelligence II - Applied Machine Learning Lecture 6 Unsupervised Learning (Clustering) A Basic Introduction to Recommendation Systems Amr S. Ghoneim (Assistant Professor, Computer Science Dept.) Helwan University Fall 2019 Lecture is based on its counterparts in the following courses (and the following resources): o Recommender Systems: An introduction, lecture at the University of Szeged (Hungary), by Dietmar Jannach (Technische Universität "TU" Dortmund, Germany) o PV254 Recommender Systems, Masaryk University (Czech Republic), Department of Machine Learning and Data Processing – Faculty of Informatics o Recommender Systems: An Introduction. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, 2010. o Recommender Systems Handbook. F. Ricci, L. Rokach, B. Shapira, P. B. Kantor, 2015 (second edition). Today’s Key Concepts o Machine Learning Approaches & their corresponding Applications o Motivation o the Jam experiment .. o Examples of Applications o Recommendation Systems & Education o Recommendation Systems o An “often-cited” Problem Characterization o Various Paradigms o Collaborative Filtering (CF) o User-based Nearest Neighbor CF o Pearson Correlation o Item-based CF o Content-based Recommendation o o o o o o o o o What is the “Content” ? Content Representation Item Similarities Term-Frequency (𝑇𝐹) Inverse Document Frequency (𝐼𝐷𝐹) Example: TF–IDF Representation Improving the Vector Space Model Cosine Similarity Recommending Items Machine Learning Approaches & their corresponding Applications Motivation .. the Jam experiment .. Motivation & Examples of Applications o Information overload; o Many choicesavailable .. o “the paradox of choice” (Choice Overload, .. “Why more is less”) .. o Recommender System; o Provides aid .. o Set of items + user “context” ⇒ selection of items (predicted to be “good” for the user) Movies, online videos, music, books, software (apps), products in general, people (dat

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