CS8080-INFORMATION RETRIEVAL TECHNIQUES Syllabus 2017 Regulation

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CS8080- INFORMATION RETRIEVAL TECHNIQUES Syllabus 2017 Regulation

INFORMATION RETRIEVAL TECHNIQUES Syllabus 2017 Regulation,CS8080-INFORMATION RETRIEVAL TECHNIQUES Syllabus 2017 Regulation

CS8080                      INFORMATION RETRIEVAL TECHNIQUES                     L T P C                                                                                                                            3 0 0 3

OBJECTIVES:

  • To understand the basics of Information Retrieval.
  • To understand machine learning techniques for text classification and clustering.
  • To understand various search engine system operations.
  • To learn different techniques of recommender system.

UNIT I INTRODUCTION                                                   9

Information Retrieval – Early Developments – The IR Problem – The Users Task – Information versus Data Retrieval – The IR System – The Software Architecture of the IR System – The Retrieval and Ranking Processes – The Web – The e-Publishing Era – How the web changed Search – Practical Issues on the Web – How People Search – Search Interfaces Today – Visualization in Search Interfaces.

UNIT II MODELING AND RETRIEVAL EVALUATION     9

Basic IR Models – Boolean Model – TF-IDF (Term Frequency/Inverse Document Frequency) Weighting – Vector Model – Probabilistic Model – Latent Semantic Indexing Model – Neural Network Model – Retrieval Evaluation – Retrieval Metrics – Precision and Recall – Reference Collection – User-based Evaluation – Relevance Feedback and Query Expansion – Explicit Relevance Feedback.

UNIT III TEXT CLASSIFICATION AND CLUSTERING    9

A Characterization of Text Classification – Unsupervised Algorithms: Clustering – Naïve Text Classification – Supervised Algorithms – Decision Tree – k-NN Classifier – SVM Classifier – Feature Selection or Dimensionality Reduction – Evaluation metrics – Accuracy and Error – Organizing the classes – Indexing and Searching – Inverted Indexes – Sequential Searching – Multi-dimensional Indexing.

UNIT IV WEB RETRIEVAL AND WEB CRAWLING        9

The Web – Search Engine Architectures – Cluster based Architecture – Distributed Architectures – Search Engine Ranking – Link based Ranking – Simple Ranking Functions – Learning to Rank – Evaluations — Search Engine Ranking – Search Engine User Interaction – Browsing – Applications of a Web Crawler – Taxonomy – Architecture and Implementation – Scheduling Algorithms – Evaluation.

UNIT V RECOMMENDER SYSTEM                                 9

Recommender Systems Functions – Data and Knowledge Sources – Recommendation Techniques – Basics of Content-based Recommender Systems – High Level Architecture – Advantages and Drawbacks of Content-based Filtering – Collaborative Filtering – Matrix factorization models – Neighborhood models.

                                                                                                      TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the students will be able to:

  • Use an open source search engine framework and explore its capabilities
  • Apply appropriate method of classification or clustering.
  • Design and implement innovative features in a search engine.
  • Design and implement a recommender system.

TEXT BOOKS:

  1. Ricardo Baeza-Yates and Berthier Ribeiro-Neto, ―Modern Information Retrieval: The Concepts and Technology behind Search, Second Edition, ACM Press Books, 2011.
  2. Ricci, F, Rokach, L. Shapira, B.Kantor, ―Recommender Systems Handbook, First Edition, 2011.

REFERENCES:

  1. C. Manning, P. Raghavan, and H. Schütze, ―Introduction to Information Retrieval, Cambridge University Press, 2008.
  2. Stefan Buettcher, Charles L. A. Clarke and Gordon V. Cormack, ―Information Retrieval: Implementing and Evaluating Search Engines, The MIT Press, 2010.

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