Course

Unit 1

Introduction

Boolean retrieval – The Information Retrieval problem – Inverted index – Processing Boolean queries – Extended Boolean model – Ranked retrieval – Document delineation – Determining vocabulary of terms – Skip pointers – Search structures for dictionaries – Wildcard queries – Spelling correction – Phonetic correction.

Unit 2

UNIT II – Index Construction

Blocked Sort-Based Indexing (BSBI) – Single-Pass In-Memory Indexing (SPIMI) – Distributed indexing – Dynamic indexing – Statistical properties of terms in IR – Dictionary compression – Postings file compression.

Unit 3

Vector Space Model and Evaluation

Term frequency and weighting – TF-IDF – Vector space model – Queries as vectors – Computing vector scores – Cosine similarity – IR system evaluation – Standard text collections – Evaluation of unranked retrieval sets – Evaluation of ranked retrieval sets (Precision, Recall, F-measure, MAP, NDCG).

Unit 4

Probabilistic And Language Models

Probability Ranking Principle – Binary Independence Model – Appraisal of probabilistic models – Language models – Query likelihood models – Smoothing techniques – Merits and demerits of language models.

Unit 5

Web Search

Characteristics of the Web – Search user experience – Index size and estimation – Near-duplicates and shingling – Web crawler architecture – URL frontier – Link analysis – Web as a graph – PageRank algorithm – Hubs and Authorities (HITS algorithm).

Text Books / References

Text Books:

1. Manning C, Raghavan P, Schutze H, “Introduction to Information Retrieval”, Cambridge University Press, New Delhi, 2008.
2. Ricardo Baeza-Yates, Berthier Ribeiro-Neto, “Modern Information Retrieval: The Concepts and Technology behind Search”, Addison Wesley, USA, 2011.

References:

1. Bruce Croft W, Metzler D, Strohman T, “Search Engines: Information Retrieval in Practice”, Addison Wesley, USA, 2009.
2. Gerald K, “Information Retrieval Architecture and Algorithms”, Springer, Heidelberg, 2013.
3. Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack, “Information Retrieval: Implementing and 4 Evaluating Search Engines”, MIT Press, Cambridge, USA, 2016.
4. Hang Li, “Learning to Rank for Information Retrieval and Natural Language Processing”, 2nd Edition,   Morgan & Claypool Publishers, USA, 2014.

This course introduces the fundamental principles and practical techniques used in modern Information Retrieval (IR) systems. It covers Boolean and ranked retrieval models, index construction techniques, compression strategies, vector space and probabilistic retrieval models, and evaluation methodologies. The course further explores web-scale search engine architecture, crawling mechanisms, and link analysis algorithms such as PageRank and HITS. Emphasis is given to both theoretical foundations and real-world search engine design considerations.

Course Outcomes: After successful completion of the course, students will be able to

• CO1: Explain the basic concepts of Information Retrieval, Boolean models, and indexing mechanisms.
• CO2: Apply index construction techniques and compression methods for efficient retrieval.
• CO3: Implement vector space and probabilistic retrieval models to compute document relevance.
• CO4: Evaluate IR systems using standard performance metrics and benchmark datasets.
• CO5: Analyze web search engine architecture, crawling mechanisms, and link analysis algorithms.

CO-PO Mapping: