{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

124.11.lec10 - CS 124/LINGUIST 180 From Click to edit...

Info icon This preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
Click to edit Master subtitle style 1/10/09 Dan Jurafsky Lecture 10: Information Retrieval III Evaluation and Assorted IR/Web topics CS 124/LINGUIST 180: From Thanks to Chris Manning for these slides from his CS 276 Information Retrieval and Web Search class!
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Slide from Chris Manning's 276 class This lecture How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall Remaining assorted IR/Web topics: Results summaries: Making our good results usable to a user Relevance Feedback Searching the Web Slide from Chris Manning's 276 class
Image of page 2
Click to edit Master subtitle style 1/10/09 Evaluating search engines
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Slide from Chris Manning's 276 class Measures for a search engine How fast does it index Number of documents/hour (Average document size) How fast does it search Latency as a function of index size Expressiveness of query language Ability to express complex information needs Speed on complex queries Uncluttered UI Is it free?
Image of page 4
Slide from Chris Manning's 276 class Measures for a search engine All of the preceding criteria are measurable : we can quantify speed/size; we can make expressiveness precise The key measure: user happiness What is this? Speed of response/size of index are factors But blindingly fast, useless answers won’t make a user happy Need a way of quantifying user happiness
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Slide from Chris Manning's 276 class Measuring user happiness Issue: who is the user we are trying to make happy? Depends on the setting Web engine : user finds what they want and return to the engine Can measure rate of return users eCommerce site : user finds what they want and make a purchase Is it the end-user, or the eCommerce site, whose happiness we measure? Measure time to purchase, or fraction of searchers who become buyers?
Image of page 6
Slide from Chris Manning's 276 class Measuring user happiness Enterprise (company/govt/academic): Care about “user productivity” How much time do my users save when looking for information? Many other criteria having to do with breadth of access, secure access, etc.
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Slide from Chris Manning's 276 class Happiness: elusive to measure Most common proxy: relevance of search results But how do you measure relevance? We will detail a methodology here, then examine its issues Relevant measurement requires 3 elements: 1. A benchmark document collection 2. A benchmark suite of queries 3. A usually binary assessment of either Relevant or Nonrelevant for each query and each document Slide from Chris Manning's
Image of page 8
Slide from Chris Manning's 276 class Evaluating an IR system Note: the information need is translated into a query Relevance is assessed relative to the information need not the query E.g., Information need : I'm looking for information on whether drinking red wine is more effective at reducing your risk of heart attacks than white wine.
Image of page 9

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern