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).
Curriculum
teacher profile teaching materials
The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Mutuazione: 20801798 SISTEMI INTELLIGENTI PER INTERNET in Ingegneria informatica LM-32 SANSONETTI GIUSEPPE
Programme
The course will examine various methods for designing, implementing, and testing adaptive systems on the Web, realized through Artificial Intelligence techniques. We will pay particular attention to Information Retrieval systems, such as search engines, and new and emerging technologies suitable for developing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including Sentiment Analysis techniques, User Modeling methods needed for developing personalized research tools, Recommender Systems, and the detection and analysis of Online Communities and Social Networks (such as Facebook and Twitter).Core Documentation
Lectures will cover topics dealt with in scientific papers and reference texts.The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Type of delivery of the course
In-person classes and in-class labs.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, project evaluation. teacher profile teaching materials
The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Programme
The course will examine various methods for designing, implementing, and testing adaptive systems on the Web, realized through Artificial Intelligence techniques. We will pay particular attention to Information Retrieval systems, such as search engines, and new and emerging technologies suitable for developing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including Sentiment Analysis techniques, User Modeling methods needed for developing personalized research tools, Recommender Systems, and the detection and analysis of Online Communities and Social Networks (such as Facebook and Twitter).Core Documentation
Lectures will cover topics dealt with in scientific papers and reference texts.The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Reference Bibliography
- Lecture slides. - Bruce Croft, Donald Metzler, and Trevor Strohman. 2010. Search Engines: Information Retrieval in Practice (1st. ed.). Addison-Wesley Publishing Company, USA. - Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2021. Recommender Systems: An Introduction (1st. ed.). Cambridge University Press, USA. - Francesco Ricci, Lior Rokach, and Bracha Shapira (eds.). 2022. Recommender Systems Handbook (3rd. ed.). Springer, USA.Type of delivery of the course
In-person classes and in-class labs.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, project evaluation. teacher profile teaching materials
The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Programme
The course will examine various methods for designing, implementing, and testing adaptive systems on the Web, realized through Artificial Intelligence techniques. We will pay particular attention to Information Retrieval systems, such as search engines, and new and emerging technologies suitable for developing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including Sentiment Analysis techniques, User Modeling methods needed for developing personalized research tools, Recommender Systems, and the detection and analysis of Online Communities and Social Networks (such as Facebook and Twitter).Core Documentation
Lectures will cover topics dealt with in scientific papers and reference texts.The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.
Reference Bibliography
- Lecture slides. - Bruce Croft, Donald Metzler, and Trevor Strohman. 2010. Search Engines: Information Retrieval in Practice (1st. ed.). Addison-Wesley Publishing Company, USA. - Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2021. Recommender Systems: An Introduction (1st. ed.). Cambridge University Press, USA. - Francesco Ricci, Lior Rokach, and Bracha Shapira (eds.). 2022. Recommender Systems Handbook (3rd. ed.). Springer, USA.Type of delivery of the course
In-person classes and in-class labs.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written test, project evaluation.