Information Retrieval

Course Title: Information Retrieval

Course No: CSIT.425.1

Nature of the Course: Theory + Lab

Semester: 8

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 10 + 10

Credit Hours: 3

Course Description

This is a undergraduate-level introductory course for information retrieval. It will cover algorithms, design, and implementation of modern information retrieval systems. Topics include: retrieval system design and implementation, text analysis techniques, retrieval model, search evaluation, retrieval feedback, applications in web information management.

Course Objectives

Learn the information retrieval models. Be familiar with Web Search Engine. Be exposed to Link Analysis. Understand Hadoop and Map Reduce. Learn document text mining techniques.

Course Contents

1. Introduction

6 hrs

1.1. IR Fundamentals

Introduction

History of IR

Components of IR

Issues

Open source Search engine Frameworks

1.2. IR and the Web

The impact of the web on IR

The role of artificial intelligence (AI) in IR

IR Versus Web Search

Components of a Search engine

Characterizing the web

2. Information Retrieval

12 hrs

2.1. Retrieval Models and Indexing

Boolean and vector-space retrieval models

Term weighting - TF-IDF weighting

Cosine similarity

Preprocessing

Inverted indices

Efficient processing with sparse vectors

2.2. Advanced Retrieval Models

Language Model based IR

Probabilistic IR

Latent Semantic Indexing

Relevance feedback

Pseudo-relevance feedback and query expansion

3. Web Search Engine-Crawling

8 hrs

3.1. Web Search Overview

Web search overview

Web structure

The user

Paid placement

Search engine optimization/spam

Web size measurement

3.2. Crawling and Indexing

Web Search Architectures

Crawling

Meta-crawlers

Focused Crawling

Web indexes

Near-duplicate detection

Index Compression

XML retrieval

4. Web Search

10 hrs

4.1. Link Analysis and Ranking

Link Analysis

Hubs and authorities

Page Rank and HITS algorithms

Searching and Ranking

Relevance Scoring and ranking for Web

Similarity

Hadoop & Map Reduce

Evaluation

4.2. Personalization and Advanced Features

Personalized search

Collaborative filtering and content-based recommendation of documents and products

Handling invisible Web

Snippet generation

Summarization

Question Answering

Cross-Lingual Retrieval

5. Document Text Mining

9 hrs

5.1. Text Classification

Information filtering; organization and relevance feedback

Text Mining

Text classification and clustering

Naive Bayes categorization algorithm

Decision trees categorization algorithm

Nearest neighbor categorization algorithm

5.2. Clustering Algorithms

Agglomerative clustering

K-means

Expectation maximization (EM)

Laboratory Works

Student should implement IR algorithms discussed in the course by using weighting and similarity measures. Students also need to practice Web Search, Clustering, Classification, and Recommendation Generation Algorithms.

1.IR Algorithms
2.Web Search and Mining

Text Books

1.C. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008.

Reference Books

1.Ricardo Baeza, Yates and Berthier Ribeiro, Neto, Modern Information Retrieval: The Concepts and Technology behind Search 2nd Edition, ACM Press Books 2011.
2.Bruce Croft, Donald Metzler and Trevor Strohman, Search Engines: Information Retrieval in Practice, 1st Edition Addison Wesley, 2009.
3.Mark Levene, An Introduction to Search Engines and Web Navigation, 2nd Edition Wiley, 2010.

Notes:

This syllabus follows the official CSIT curriculum of Far Western University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://cdc.fwu.edu.np/faculties.html

Information Retrieval

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TabFlux . Information Retrieval . FWU . BSc. CSIT