Dec 03, 2022  
OHIO University Graduate Catalog 2020-21 
    
OHIO University Graduate Catalog 2020-21 [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. The course focuses 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 also covers 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 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 for a Web search engine.
  • Students will be able to evaluate the performance of IR systems.
  • Students will be able to acquire knowledge of query expansion techniques and relevance feedback.
  • Students will be able to use probabilistic IR models and language models for IR.
  • Students will be able to use and compare analysis algorithms such as PageRank and Hubs and Authorities.
  • Students will be able to implement text clustering and classification algorithms and explain their relevance for IR.
  • Students will be able to implement major indexing techniques and evaluate their impact on query processing.
  • Students will be able to summarize approaches for recommender systems and collaborative filtering.
  • Students will be able to explain basic statistical properties of large text collections and evaluate their impact on the design of effective IR engines.
  • Students will be able to explain Boolean retrieval models and their two major extensions: phrase search and proximity search.
  • Students will be able to explain major techniques for personalized IR.
  • Students will be able to use the vector space model approach for ranked information retrieval.
  • Students will be able to explain the importance of natural language processing tools for IR, especially for language identification, tokenization, and word sense disambiguation.
  • Students will be able to use third party packages for the implementation of indexing and search engines.



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