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 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.
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
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.
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.