Learning outcomes

After completing the course a student is expected to be able to:

-be familiar with essential models of Information Retrieval and able to assess their strengths
-be familiar with essential classification and clustering and able to assess their strengths
-be familiar with essential methods of search and recommendation in social media and personalization
-use and assess tools implementing these methods and interpret the results given by these tools

Contents

Queries and interfaces. Retrieval Models: Boolean and fuzzy Boolean, Vector Space, classical Probabilistic, and Language Models. Fuzzy string matching methods. Classification and clustering methods. Methods of search and recommendation in social media and personalization.

Teaching methods

Teaching language

Evaluation

Study materials

• Croft, Metzler & Strohman, Search Engines: Information Retrieval in Practice. Addison-Wesley. 2010.
• Manning, Raghavan & Schuetze, Introduction to Information retrieval. Cambridge University Press. 2009.

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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