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Human Computer Information Retrieval HCIR

Human-Computer Information Retrieval (HCIR) merges human intelligence with information retrieval techniques, enhancing how users interact with information in modern digital environments. Coined by Gary Marchionini, HCIR explores the dynamics of content, which is increasingly multimedia and contextual, necessitating innovative search strategies beyond traditional methods. By supporting exploratory searches and user-driven relevance, HCIR aims to make information access engaging and efficient, evolving traditional IR practices to address complex user needs and behaviors within ever-changing cyberinfrastructure.

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Human Computer Information Retrieval HCIR

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  1. Human Computer Information RetrievalHCIR WHIM- Spring ‘10 By:-Enza Desai

  2. What is HCIR? • Study of IR techniques that brings human intelligence into search process. • Coined by Gary Marchionini. • Combines various aspects of IR and HCI • Syminforosis-people as organic information processors continuously engaged with information in emerging cyberinfrastructure • Actions and interactions with information: how people find and use information when mediated by technology

  3. Why its needed? • Content is changing from plain text to multimedia, multilingual, recommendations, temporal (blogs, wikis) and conditional–which are also dynamic • Cannot match queries to static indexes • Relationship between content • Content has history thus need context retrieval

  4. Continued.. • People base is also changing • Understanding over retrieval • Need ways to bring human intelligence and attention into the search process. • Supports Exploratory searches.

  5. Goals of HCIR • Get people closer to the information they need • Increase user responsibility & control • Flexible architectures • Systems be part of information cycle • Support the entire information life cycle • Support tuning by end users • Engaging and fun to use.

  6. Traditional IR and HCIR?

  7. World with HCIR…

  8. Evaluation methods • Beyond Recall and Precision • Newer evaluation metrics like • Utility • Search engine Satisfaction (SES) • User Experience (UX) • Relative Relevance (RR) • Ranked Half Life (RHL) • Etc..

  9. Some implementation • Faceted search: navigate information hierarchically, going from a category to its sub-categories • Spelling suggestions and automatic query reformulation in search engines. • Interactive User Interaction and Visual representation of data • Relevance feedback

  10. Conclusion • Search has been limited to a single text box. • Traditional IR doesn’t address hard search problems. • Use human intelligence to lead the user to relevant results • Minimizes costs of time, mouse clicks, or context shift. • Faceted search is a common approach to addressing search problems.

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