Mar 28, 2024  
OHIO University Graduate Catalog 2019-20 
    
OHIO University Graduate Catalog 2019-20 [Archived Catalog]

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CS 6860 - Information Retrieval and Web Search


This course covers the design, implementation, and evaluation of modern information retrieval (IR) systems, such as Web search engines. It will focus on the underlying retrieval models, algorithms, and system implementations, such as vector-space and probabilistic retrieval models, as well as the PageRank algorithm used by Google. The course will also cover more advanced topics in information retrieval, including document categorization and clustering, recommender systems, collaborative filtering, and personalized search.

Requisites:
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes:
  • Students will acquire knowledge of query expansion techniques and relevance feedback.
  • Students will be able to build a basic Web search engine, starting from a web crawler to a ranking model that integrates vector space measures with link analysis.
  • Students will be able to build a basic web interface to a basic Web search engine.
  • Students will be able to evaluate the performance of IR systems.
  • Students will develop a theoretical understanding of alternative models to IR such as probabilistic IR and language models for IR.
  • Students will develop an understanding of link analysis algorithms such as PageRank and Hubs and Authorities.
  • Students will develop an understanding of text clustering and classification algorithms and their relevance for IR.
  • Students will develop and understanding the major indexing techniques and will be able to evaluate their impact on query processing.
  • Students will gain a basic understanding of reccomender systems and collaborative filtering.
  • Students will gain a theoretical understanding of basic statistical properties of large text collections and their impact on the design of effective IR engines.
  • Students will gain an understanding of Boolean retrieval models and their two major extensions: phrase search and proximity search.
  • Students will gain an understanding of major techniques for personalized IR.
  • Students will gain an understanding of the vector space model approached to ranked information retrieval.
  • Students will understand the importance of natural language processing tools for IR, especially for language identification, tokenization, and word sense disambiguation.



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