Information Retrieval Techniques for BE Anna University R17 CBCS (VIII-CSE/IT/Prof. Elec.-V- CS8080)

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UNIT I INTRODUCTION Information Retrieval - Early Developments - The IR Problem - The User’s 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. (Chapter - 1) UNIT II MODELING AND RETRIEVAL EVALUATION 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. (Chapter - 2) UNIT III TEXT CLASSIFICATION AND CLUSTERING 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. (Chapter - 3) UNIT IV WEB RETRIEVAL AND WEB CRAWLING 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. (Chapter - 4) UNIT V RECOMMENDER SYSTEM 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. (Chapter - 5)

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Pages: 168 Edition: 2023 Vendors: Technical Publications