CS260 Search Björn Hartmann University of California, Berkeley EECS, Computer Science Division Fall 2010 Monday, November 22, 2010
Wednesday (before you leave campus) No reading responses . Instead, submit 2 paragraphs about your evaluation plan: What questions are you trying to answer? How will you operationalize the questions? Who will you recruit? How many participants? When will you test? What will the test protocol be? How will you analyze your results? CS260 - UC Berkeley Fall 2010 2 Monday, November 22, 2010
End game... Wed 11/24 Lecture Mobile Interation, Course Survey Due Evaluation Plan Mon 11/29 Lecture Usable Security Wed 12/1 Lecture Course Summary Fri 12/3 Due Paper Draft (Pilot test data) Wed 12/8 Due Final Presentations, 3pm, 306 Soda Mon 12/13 Due Final Paper CS260 - UC Berkeley Fall 2010 3 Monday, November 22, 2010
Search (most material from M. Hearst, Search User Interfaces & SIMS 141) CS260 - UC Berkeley Fall 2010 4 Monday, November 22, 2010
Standard Model of Search Process Task Information Need Verbal Form Corpus Corpus Query Corpus Search Engine Query Refinement Results A. Broder. A taxonomy of web search. SIGIR Forum , 36(2):3–10, 2002. http://searchuserinterfaces.com/book/sui_ch3_models_of_information_seeking.html CS260 - UC Berkeley Fall 2010 5 Monday, November 22, 2010
Berry-Picking Model Query 3 Query 2 Query 4 Query 1 M.J. Bates. The design of browsing and berrypicking techniques for the on-line search interface. Online Review, 13(5):407–431, 1989. CS260 - UC Berkeley Fall 2010 6 Monday, November 22, 2010
CS260 - UC Berkeley Fall 2010 7 (cc) Thomas Hawk - http://www.flickr.com/photos/thomashawk/85441961/ Monday, November 22, 2010
Searching vs. Browsing “Browsing is a retrieval process where the users navigate through the text database by following links from one piece of text to the next, aiming to utilize two human capabilities ... the greater ability to recognize what is wanted over being able to describe it and ... the ability to skim or perceive at a glance. This allows users to evaluate rapidly rather large amounts of text and determine what is useful.” [Hertzum and Frokjaer, 1996] CS260 - UC Berkeley Fall 2010 8 Monday, November 22, 2010
Searching vs. Browsing “Considered in cognitive terms, searching is a more analytical and demanding method for locating information than browsing, as it involves several phases, such as planning and executing queries, evaluating the results, and refining the queries, whereas browsing only requires the user to recognize promising-looking links.” A. Aula. Studying user strategies and characteristics for developing web search interfaces. PhD thesis, University of Tampere, Finland, 2005. CS260 - UC Berkeley Fall 2010 9 Monday, November 22, 2010
Information Foraging & Scent Estimating the utility of distal information sources from proximal signals. CS260 - UC Berkeley Fall 2010 10 Monday, November 22, 2010
Task: Find the most relevant HCI studies of Q&A communities CS260 - UC Berkeley Fall 2010 11 Monday, November 22, 2010
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Orienteering vs. Teleporting Orienteering : start with short, general queries, then incrementally refine based on feedback Teleporting : use one, long, specific query Examples? CS260 - UC Berkeley Fall 2010 13 Monday, November 22, 2010
Goals Fact Finding Information Gathering Browsing Transactions Other CS260 - UC Berkeley Fall 2010 14 Monday, November 22, 2010
Early Web: Directories CS260 - UC Berkeley Fall 2010 15 Monday, November 22, 2010
Yahoo Homepage, 1996 CS260 - UC Berkeley Fall 2010 16 Source: archive.org Monday, November 22, 2010
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? CS260 - UC Berkeley Fall 2010 20 Monday, November 22, 2010
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Tree Hierarchies CS260 - UC Berkeley Fall 2010 22 Monday, November 22, 2010
The Problem With Hierarchy Forces a choice of one dimension vs another Either you commit to one path, Or you have to provide many redundant combinations Examples Each topic followed by all time periods followed by all locations AND Each topic followed by all locations followed by all time periods AND Each location followed by all topics followed by all time periods … etc Slide from: M. Hearst, SIMS141 CS260 - UC Berkeley Fall 2010 23 Monday, November 22, 2010
Facets Sets of categories, each of which describe a different aspect of the objects in the collection. Each of these can be hierarchical. (Not necessarily mutually exclusive nor exhaustive, but often that is a goal.) GeoRegion Time/Date Topic Role + + + CS260 - UC Berkeley Fall 2010 Slide from: M. Hearst, SIMS141 24 Monday, November 22, 2010
Facet example: Recipes COOKING METHOD INGREDIENT Stir-fry Chicken Red Bell Pepper Curry COURSE Main Course CUISINE Thai Slide from: M. Hearst, SIMS141 CS260 - UC Berkeley Fall 2010 25 Monday, November 22, 2010
Hierarchical Faceted Metadata A simplification of knowledge representation Does not represent relationships directly BUT can be understood well by many people when browsing rich collections of information. CS260 - UC Berkeley Fall 2010 26 Monday, November 22, 2010
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Query Formulation CS260 - UC Berkeley Fall 2010 33 Monday, November 22, 2010
Query Formulation Most people have an incomplete mental model of query formulation Plenty of searches for “Yahoo” or “Google” Sensitivity to ordering? Boolean connectors? CS260 - UC Berkeley Fall 2010 34 Monday, November 22, 2010
Shortcuts “Zero-click” Results CS260 - UC Berkeley Fall 2010 35 Monday, November 22, 2010
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What is the command language for the Google search box? CS260 - UC Berkeley Fall 2010 39 Monday, November 22, 2010
Search Result Visualization Document Surrogates CS260 - UC Berkeley Fall 2010 40 Monday, November 22, 2010
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Evernote CS260 - UC Berkeley Fall 2010 42 Monday, November 22, 2010
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Domain-Specific Search CS260 - UC Berkeley Fall 2010 44 Monday, November 22, 2010
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Also see: CS260 - UC Berkeley Fall 2010 Myers et al., 47 Apatite, Jadeite Monday, November 22, 2010
Code Search Engines Assieme, Hoffman, UIST07 CS260 - UC Berkeley Fall 2010 48 Monday, November 22, 2010
Collaborative Search CS260 - UC Berkeley Fall 2010 49 Monday, November 22, 2010
Collaborative Search Many search tasks are completed by groups(e.g., plan an itinerary for our vacation). Search user interfaces assume single users. How can user interfaces enhance and support group information seeking? CS260 - UC Berkeley Fall 2010 50 Monday, November 22, 2010
CS260 - UC Berkeley Fall 2010 http://research.microsoft.com/en-us/news/features/searchtogether.aspx 51 Monday, November 22, 2010
Social Search Re-rank search results based on social graph information (e.g., links previously published by your friends) Outsource IR to social graph: “Dear Lazyweb: ...” CS260 - UC Berkeley Fall 2010 52 Monday, November 22, 2010
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hci.berkeley.edu/cs260-fall10 Monday, November 22, 2010
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