Syllabus - Web & Information Retrieval (CSIT-802(C))

Computer Science & Information Technology

Web & Information Retrieval (CSIT-802(C))

VIII-Semester

Unit-I

Introduction

Information versus data retrieval, the retrieval process, taxonomy of Information Retrieval Models.

Unit-II

Classic Information Retrieval Techniques

Boolean Model, Vector model, Probabilistic Model, comparison of classical models. Introduction to alternative algebraic models such as Latent semantic Indexing etc.

Unit-III

Keyword based Queries, User Relevance Feedback

Query Expansion and Rewriting, Document preprocessing and clustering, Indexing and Searching: Inverted Index construction, Introduction to Pattern matching.

Unit-IV

Web Search

Crawling and Indexes, Search Engine architectures, Link Analysis and ranking algorithms such as HITS and Page Rank, Meta searches, Performance Evaluation of search engines using various measures, Introduction to search engine optimization.

Unit-V

Introduction to online IR Systems, Digital Library searches and web Personalization.

Course Objective

This course aims at introducing the area of Information Retrieval and at examining the theoretical and practical issues involved in designing, implementing and evaluating Information Retrieval systems.

Course Outcome

["To identify basic theories and analysis tools as they apply to information retrieval.", "To develop understanding of problems and potentials of current IR systems.", "To learn and appreciate different retrieval algorithms and systems.", "To apply various indexing, matching, organizing, and evaluating methods to IR problem.", "To become aware of current experimental and theoretical IR research."]

Practicals

  • Students must experiment on various information retrieval systems like page rank etc

Reference Books

  • Ricardo Baeza-Yates and Berthier Ribeiro-Neto, “Modern Information Retrieval” Pearson Education

  • C. Manning, P. Raghvan and H. Schutze, “Introduction to Information Retrieval”, Cambridge University Press.

  • Amy N. Langville and Carl D. Meyer, “Google’s PageRank and Beyond: The Science of Search Engine Rankings”, Princeton Universit Press

  • Pierre Baldi, Paolo Frasconi and PadhraicSmythe, “Modelling the internet and the web: Probabilistic methods and Algorithms”, John Wiley