Recommendation and Content Filtering in Information Systems


Title: Recommendation and content filtering in information systems

Lecturer: Ludovico Boratto

Period: february – march 2014 (10 hrs, 2 CFR)

Abstract. Thanks to the rapid evolution of the web and the development of new types of applications (from blogs, to wikis, to social networks), users are no longer passive consumers of content, but actively share resources that are of interests for them, with an ever-growing frequency.

This has brought to a rapid growth of the amount of content available on the web and to a problem, known as “information overload”, which limits the users in the research of resources that might be interesting for them. In order to overcome this problem, in the last few years several technologies that filter the enormous amount of content available in the web have been developed.

The most interesting among these technologies, known as recommender systems, is able to automatically present personalized content to users, which is of interest for them.


  1. Introduction to recommender systems
  2. Collaborative Filtering
  3. Content-based Filtering
  4. Hybrid methods
  5. Knowledge-based methods
  6. Recommender systems evaluation

Course schedule


  1. Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor – Recommender Systems Handbook (Springer)
  2. Bamshad Mobasher. Data Mining for Web Personalization
  3. Ben Schafer, Dan Frankowski, Jonathan L. Herlocker, Shilad Sen. Collaborative Filtering Recommender Systems
  4. Michael J. Pazzani, Daniel Billsus. Content-Based Recommendation Systems
  5. Barry Smyth. Case-Based Recommendation
  6. Robin D. Burke. Hybrid Web Recommender Systems
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