Deepthi Katta

Award Date


Degree Type


Degree Name

Master of Science in Computer Science


Computer Science

First Committee Member

Kazem Taghva, Chair

Second Committee Member

Ajoy K. Datta

Third Committee Member

Laxmi P. Gewali

Graduate Faculty Representative

Muthukumar Venkatesan

Number of Pages



Information Retrieval is the science of searching for information or documents based on information need from a huge set of documents. It has been an active field of research since early 19th century and different models of retrieval came in to existence to cater the information need.

This thesis starts with understanding some of the basic information retrieval models, followed by implementation of one of the most popular statistical retrieval model known as Vector Space Model. This model ranks the documents in the collection based on the similarity measure calculated between the query and the respective document. The user specifies the "information need" which is more commonly known as a "query" using the visual interface provided. The given query is then processed and the results are displayed to the user in a ranked order.

We then focus on the Relevance feedback, a technique that modifies the user query based on the characteristics of the document collection to improve the results. In this thesis, we explore different types and models of relevance feedback that can be applied to Vector Space model and how they affect the performance of the model.


Information retrieval; Internet searching; Keyword searching; Vector spaces--Data processing


Computer Sciences | Databases and Information Systems | Theory and Algorithms

File Format


Degree Grantor

University of Nevada, Las Vegas




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