CS8080 Information Retrieval Techniques

CS8080 Information Retrieval Techniques

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      Santhosh (Admin) 
Important questions 
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UNIT -1
1.Diff b/w IR and web research,information vs data retrivel
2. Architecture of IR 
3.Visualization in Search Interfaces
UNIT -2

2.RAre( Retrieval Evaluation,Metrics)
3.All relevance Feedbacks**
4.Query generation probability

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UNIT -3
1. Clustering,k-NN Classifier**SVM Classifier
2. 
Indexing and Searching (full topic)
UNIT -4
1.Search Engine Ranking,UI 
2.Cluster based, Distributed Architectures**
3. Applications of a Web Crawler (full topic),Scheduling Algorithms**
UNIT -5
1.Recommendation Techniques,high level architecture
2.Collaborative,content based filtering Filtering**(full topic)

**Most important topic 

PART-C

1.Compulsory Questions {a case study where the student will have to read and analyse the subject }
mostly asked from algorithms (OR) a situation given and you have to answer on your own

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Contact uS *These questions are expected for the exams This may or may not be asked for exams
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Syllabus

 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.
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.
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.
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.
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.

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