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Understanding Relevance in Information Retrieval

Learn about the importance of relevance in IR, factors affecting precision and recall, and strategies to improve search performance.

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Understanding Relevance in Information Retrieval

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  1. Relevance and Evaluation of IR Performance Dr. Bilal IS 530 Fall 2009

  2. Searching for Information • Imprecise • Incomplete • Tentative • Challenging

  3. Why Some Items Are Not Retrieved? • Indexing errors • Wrong search terms • Wrong database • Language variations Other (to be answered by students)

  4. Boolean Operators • OR increases recall • AND increases precision • NOT increases precision by elimination

  5. Relevance • A match between a query and information retrieved • A judgment • Can be judged by anyone who is informed of the query and views the retrieved information

  6. Relevance • Judgment is dynamic • Documents can be ranked by likely relevance • In practice, not easy to measure • Not focused on user needs

  7. Pertinence • Based on information need rather than a match between a query and retrieved documents • Can only be judged by user • May differ from relevance judgment

  8. Pertinence • Transient, varies with many factors • Not often used in evaluation • May be used as a measure of satisfaction • User-based, as opposed to relevance

  9. Relevance Judgment • Users base it on: • Topicality • Aboutness • Utility • Novelty • Satisfaction

  10. IR Performance Recall Ratio = the number of relevant documents retrieved the total number of relevant documents

  11. Recall and Precision in Practice • Inversely related • Search strategies designed for high precision or high recall (or medium) • Needs of users dictate search strategy towards recall or precision • Practice helps changing queries to favor recall or precision

  12. Recall and Precision 1.0 Recall 1.0 Precision

  13. IR Performance Precision Ratio = the number of relevant documents retrieved the total number of documents retrieved

  14. High Precision Search • Use these strategies, as appropriate: • Controlled vocabulary • Limit feature (e.g., specific fields, major descriptors, date(s), language, as appropriate) • AND operator • Proximity operators carefully • Truncation carefully

  15. High Recall Search • Use these strategies, as appropriate: • OR logic • Keyword searching • No or minimal limit to specific field(s) • Truncate • Broader terms

  16. Improving IR Performance • Good mediation of search topic before searching • User presence during search, if possible • Preliminary search judged by user • Evaluation during search (by searcher or by searcher and user)

  17. Improving IR Performance • Refinement of search strategies • Searcher to evaluate final results • User to evaluate final results

  18. Improving IR Performance • Better system design • Better indexing and word parsing • Better structure of thesauri • Better user interface (e.g., more effective help feature) • Better error recovery feedback • User-centered design

  19. Relevance in Information Science • Dr. Tefko Saracevic talk at SIS on: “Relevance in information science” • To watch the streaming video and the slide show, click on • http://mediabeast.ites.utk.edu/mediasite4/Viewer/?peid=fb8f84cb-9f82-499f-b12c-9a56ab5cf5ba

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