Subject description

  • Principles of information extraction, indexing and retrieval. Semantic content descriptions, ontologies, metadata and standards.
  • Principles and technology of search engines, usage of web search engines for retrieval of documents and multimedia content.
  • Collection of user feedback and modeling of user's preferences. User-adaptation and personalization of content and services.
  • Recommendation techniques, recommender systems and their usage.
  • Application of personalization within social networs, e-commerce, smart devices and information systems. Problems of security, privacy and trust.

The subject is taught in programs

Objectives and competences

Course provides an overwiev of personalized content and services, information search and retrieval systems, and recommender systems. It provides a knowledge on the technology and application of personalization principles in various domains.

Teaching and learning methods

Lectures, demonstrations, practical laboratory work.

Expected study results

Understanding of concepts and issues of personalization. Knowledge and understanding of semantic metadata, information retrieval,  recommender systems, and applications of personalization.

Basic sources and literature

  1. Ricci F., Rokach L., Shapira B., Kantor P.B., Recommender Systems Handbook, Springer, New York, 2011, 842 str.
  2. Baeza-Yates R., Ribeiro-Neto B., Modern Information Retrieval, ACM Press, New York, 1999, 340 str.
  3. Hand, D. Smyth, P. Principles of data mining, The MIT Press, Cambridge, Massachusetts, London, England, 2001, 546 str.

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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana

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