In this course we will present the scientific underpinnings of the field of Information Filtering and Recommender Systems. Recommender systems are now largely used, particularly in eCommerce web sites, for easing the information search and discovery processes, and increasing customer fidelity and conversion rates. Starting from basic information retrieval concepts we will investigate more advanced techniques for information filtering and decision support in recommender systems, such as those exploiting contextual information or supporting decisions taken by groups. We will provide the student with a rich and comprehensive catalogue of information search tools that can be exploited in the design and implementation of a specific Web site, such eCommerce or eGovernment applications for travel, tourism or health. Many open issues will be described, motivating and promoting new research activities. At the end of the course every student will implement some recommendation algorithms based on given academic publications to learn the evaluation methodology in the field and assess the reproducibility of these works.

Vector space model, text and vector space classification, evaluation in information retrieval, recommender systems, collaborative- and content-based filtering, hybrid recommender systems, context-dependent recommender systems, decision making, sequential recommendations, group recommendations.

This is a visiting professor course of the Vienna PhD School of informatics. Lecturer: Prof. Markus Zanker, Free University of Bozen-Bolzano.

Registration takes place in TISS