INFORMATION RETRIEVAL AND WEB SEARCH

Information Retrieval (IR) has changed considerably in the last years with the expansion of the Web (World Wide Web), the born of Web Search Engines, and the advent of data and distributed computing clouds.
During the last decade, relentless optimization of information retrieval efficiency and effectiveness has driven web search engines to new quality levels. The field of IR has thus moved from being a primarily academic discipline to being the basis underlying most people’s preferred means of information access. The course aims at presenting the scientific underpinnings of this field and some practical issue.

Data structures and algorithms, basic in linear algebra and in probability theory.

Basic IR Models
Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval
Evaluating IR Systems
Text Representation
Web Search: Crawling, Link-based algorithms and Scalability issues
Web and text mining
Recommender Systems
Information Extraction and Integration

Lecture notes.
C. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval, Cambridge University Press, 2008.

written and oral

The exam is divided into two parts. The first part is written, and aims to test with open questions the knowledge and the ability to apply and evaluate solutions in modern application contexts of information retrieval. The second part of the exam regards the critical reading and the public presentation of scientific articles on the course topics, and aims to evaluate the analytical capability of the candidate, in addition to the ability of summarizing and communication skills.

Theoretical and practical lectures.

English

The exam is divided into two parts. The first part is written, and aims to test with open questions the knowledge and the ability to apply and evaluate solutions in modern application contexts of information retrieval. The second part of the exam regards the critical reading and the public presentation of scientific articles on the course topics, and aims to evaluate the analytical capability of the candidate, in addition to the ability of summarizing and communication skills.

• Course with sustainable contents
• University credits of sustainability: 6
• Lecture notes, material for reference or for self-assessment available online or as e-book
• Use of virtual forum, blog or wiki
• E-learning, moodle platforms
• Use of open-source software