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Personalized interfaces based on recommender systems play a central role in the solution of big data problems. Recommender systems adopt data-mining and artificial-intelligence techniques to facilitate search in large amounts of digital information, and help users to identify the content that are likely to be more attractive or useful for them. Recommender systems infer such recommendations on the basis of different elements: popularity, demographic information about users, individual’s or community’s past preferences and choices, explicit ratings or comments, social networks, context of use. During the last years, recommender systems have seen an increasing adoption in various interactive services. The most famous success story of a recommender system is Netflix. The Netflix company launched in 2008 a competition (www.netflixprize.com) offering a 1 million dollar prize to anyone able to create a better recommendation system than the one adopted by Netflix itself for its video-streaming service.

This course explores the persuasiveness of recommender systems and presents several recommender algorithms, including the Netflix winner algorithm. For each algorithm, we investigate its quality in terms of accuracy and novelty of recommendations.



  1. Introduction to recommender systems
  2. Collaborative-based techniques
  3. Content-based techniques
  4. Advanced recommender systems: hybrid, context-aware, and cross-domains algorithms
  5. Evaluation of recommender systems
  6. Human-Computer interfaces for recommender systems: elicitation and explanation

Recent Announcements

  • Final Marks Challange + MiniConferences Matricola Voto 814218 30 lode 814508 30 lode 814572 24 814638 30 lode 814895 30 lode 815730 21 816479 30 lode 816939 30 817014 30 lode 817048 30 lode 817334 ...
    Posted Feb 16, 2015, 2:35 AM by Paolo Cremonesi
  • Practice Optimization Material for the practice over optimization is available here.
    Posted Jan 5, 2015, 2:48 AM by Roberto Pagano
  • Evaluation practice Material for the practice over evaluation is available here.
    Posted Nov 26, 2014, 3:15 AM by Massimo Quadrana
  • Challenge practice Dear students, tomorrow practice will focus on writing a recommender system to make a submission to the AUI Technology competition. Despite the title of the practice, concepts useful to everyone ...
    Posted Nov 19, 2014, 12:04 AM by Massimo Quadrana
  • Lenskit Practice Link to the presentation https://docs.google.com/presentation/d/1X9tKItsKrc9C1EDUb3qRSQi5s00ZxkxmRjUUSK8qd5Q/edit?usp=sharing
    Posted Nov 6, 2014, 3:41 AM by Massimo Quadrana
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