Università Roma Tre

The course will allow students to learn various methods for the design, implementation, and testing of adaptive systems on the Web, created through Artificial Intelligence techniques, with particular reference to Machine Learning techniques. Specific attention will be paid to Information Retrieval systems, such as search engines, crawlers and document feeds. Classic retrieval models will be studied, such as the Vector Space Model and probabilistic models, document ranking techniques, as well as the PageRank algorithm used by Google. Machine Learning methods in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods necessary for personalized search, and social search applications involving communities of individuals in activities such as content tagging and question answering. The techniques for analyzing social networks (e.g., Facebook and Twitter) will be explored, which will allow us to explore phenomena such as the spread of fake news, the filter bubble, and the polarization of users. Finally, Recommender Systems will be studied, from basic algorithms (e.g., collaborative filtering) to application scenarios (e.g., movies, books, music artists and songs).