Programme

The course will examine various methods for the design, implementation, and testing of adaptive systems on the Web, realized through Artificial Intelligence techniques. Particular attention will be paid to Information Retrieval systems, such as search engines, and to new and emerging technologies suitable for realizing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space model 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 techniques for Sentiment Analysis, User Modeling methods needed for developing personalized research tools and recommender systems, identifying and analyzing online communities, and social networks (such as Facebook and Twitter). Finally, statistical methods for the experimental evaluation of the aforementioned systems will be described.

Core Documentation

Lectures will cover topics dealt with in scientific papers and reference texts.
The teacher will make available the slides from the lectures through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of further texts for passing the exam.

Reference Bibliography

- Brusilovsky, P., Kobsa, A. e Nejdl, W. (editors) "The Adaptive Web: Methods and Strategies for Web Personalization", Springer, LNCS 4321, 2007. - Croft, W.B., Metzler, D. e Strohman, T. "Search Engines: Information Retrieval in Practice", Pearson, 2010. - Manning, C.D., Raghavan, P. e Schutze, H. "Introduction to Information Retrieval", Cambridge University Press, 2008. - Jannach, D., Zanker, M., Felfernig, A. e Friedrich, G. "Recommender Systems" An Introduction", Cambridge University Press, 2011. - Cohen, P. "Empirical Methods for Artificial Intelligence", The MIT Press, 1995.

Type of delivery of the course

The teaching methods and tools that will be used to achieve the expected learning outcomes will essentially be: - lectures; - exercises; - seminars; - projects to be carried out individually or in groups, under the guidance of the teacher.

Attendance

Attending the course will be optional, carrying out the projects will be mandatory.

Type of evaluation

The course will be organized in a tutorial section (which will cover about three-quarters of the course) and a remaining seminar/project section. The examination of the acquisition of the concepts taught during the tutorial section will take place through a written exam of about 2 hours. This exam will be organized into four open questions and exercises aimed at verifying the level of effective understanding of the concepts covered in the course, and whose slides will be available on the course website. It is also required to take a second test (of 20 minutes), consisting of ten T/F questions, aimed at verifying the introductory knowledge of the topics covered in the seminar section, whose slides will be available on the course website. This test will have to be passed (it will be sufficient to achieve a score higher than a threshold value) and will not contribute to the final grade. All examination texts from previous years will be available in the paper version and may be requested to the teacher.