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Understanding User Goals in
       Web Search
        Daniel E. Rose
        Danny Levinson
Not only what, but also why

● previous work on information-seeking behavior

● authors' hierarchy of search goals

● how to classify queries

● analysis of results

● applicability of model
LIS precedents
 ● Bates
    ○ "Information Search
      Tactics" (1979)
    ○ "The Design of
      Browsing and
      Berrypicking
      Techniques for the
      Online
      Search       Interface
      " (1989)

                               Above: Bates' berrypicking model

                               Left: the traditional model of document recovery
                               Diagrams are recreations of figures that appear in "The
                               Design of Browsing and Berrypicking Techniques for the
                               Online Search Interface."
● Belkin, Oddy, and Brooks
   ○ "ASK for Information Retrieval" (1982)
       ■ from "Part I. Background and Theory"
■ from "Part II. Results of a Design Study"
● Broder
   ○ "A Taxonomy of Web Search" (2002)
Process

● developed flat list of user goals

● used list to classify queries in test set

● revised categories as necessary--developing
  hierarchical classification

● manually classified three sets of queries
   ○ Each set of approximately 500 queries was randomly selected
     from AltaVista query logs on a different day, and at a different
     time of the day, than the other two.
Search goal hierarchy
Search goal hierarchy (continued)
Manual query classification
Objects of consideration:        "We need to know the relative
  ●     the query itself         prevalence of various goals. And
  ● results returned by the      if we hope to infer goals
    search engine                automatically in the future, we
  ● results clicked on by the    need to know that it is possible
    user                         to do so manually" (16).
  ● further searches or other
    actions by the user
Do these documents provide       "Once we could successfully
"sufficient information for a    classify queries manually, we
human to consistently classify   would be able to provide training
queries according to our goal    data for a future automatic
framework" (16)?                 classification system" (16).
Final Fantasy example
Time   Delta t   Event          Details
                                                          What kind of a site did the
                                                          user who searched for "final
36     36        result click   pg 1, pos 1 http://www.
                                ffonline.com              fantasy" intend to find--a site
                                                          that sells a version of the
113    77        query          pg 1 final fantasy        game, one that lists the
118    5         result click   pg 1, pos 8 http://www.
                                                          game's "official" rules, or one
                                eyesonff.com              that provides less exact
147    29        result click   pg 1, pos 8 http://www.   information about the game?
                                eyesonff.com


 Examining the results returned by
 two search engines for this query,
 and the user's subsequent clicks, the
 authors conclude that the goal of this
 search was "undirected" information.
Results
Results (continued)
Applications and limitations



● "If our findings about    ● "One issue is that we
  the relatively small        have no way of knowing
  number of navigational      conclusively whether the
  queries are accurate,       goal we inferred for a
  they suggest that much      query is in fact the user’s
  of the attention in the     actual goal. In the future,
  commercial search           we would like to combine
  engine world may be         our work with user
  misdirected" (18).          studies, including
                              qualitative data such as
                              diary reports of user
                              goals" (19).
Questions
  ● How does Rose and Levinson's user goals paradigm
    compare to the idea of document relevance? Does a
    result need to meet a search goal in order for the user
    to perceive it as relevant, or simply convey information
    related to the search query?
  ● In what ways does their user goals model take into
    account query refinement and/or query expansion?
  ● What is the user's goal in the Aloha/American Airlines
    scenario (outlined on slides 8 and 9)?
  ● How would you apply Rose and Levinson's research to
    the following questions:
      ○ Why does a search occur?
      ○ What is its purpose?
      ○ How does it proceed?

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Zoe's presentation

  • 1. Understanding User Goals in Web Search Daniel E. Rose Danny Levinson
  • 2. Not only what, but also why ● previous work on information-seeking behavior ● authors' hierarchy of search goals ● how to classify queries ● analysis of results ● applicability of model
  • 3. LIS precedents ● Bates ○ "Information Search Tactics" (1979) ○ "The Design of Browsing and Berrypicking Techniques for the Online Search Interface " (1989) Above: Bates' berrypicking model Left: the traditional model of document recovery Diagrams are recreations of figures that appear in "The Design of Browsing and Berrypicking Techniques for the Online Search Interface."
  • 4. ● Belkin, Oddy, and Brooks ○ "ASK for Information Retrieval" (1982) ■ from "Part I. Background and Theory"
  • 5. ■ from "Part II. Results of a Design Study"
  • 6. ● Broder ○ "A Taxonomy of Web Search" (2002)
  • 7. Process ● developed flat list of user goals ● used list to classify queries in test set ● revised categories as necessary--developing hierarchical classification ● manually classified three sets of queries ○ Each set of approximately 500 queries was randomly selected from AltaVista query logs on a different day, and at a different time of the day, than the other two.
  • 9. Search goal hierarchy (continued)
  • 10. Manual query classification Objects of consideration: "We need to know the relative ● the query itself prevalence of various goals. And ● results returned by the if we hope to infer goals search engine automatically in the future, we ● results clicked on by the need to know that it is possible user to do so manually" (16). ● further searches or other actions by the user Do these documents provide "Once we could successfully "sufficient information for a classify queries manually, we human to consistently classify would be able to provide training queries according to our goal data for a future automatic framework" (16)? classification system" (16).
  • 11. Final Fantasy example Time Delta t Event Details What kind of a site did the user who searched for "final 36 36 result click pg 1, pos 1 http://www. ffonline.com fantasy" intend to find--a site that sells a version of the 113 77 query pg 1 final fantasy game, one that lists the 118 5 result click pg 1, pos 8 http://www. game's "official" rules, or one eyesonff.com that provides less exact 147 29 result click pg 1, pos 8 http://www. information about the game? eyesonff.com Examining the results returned by two search engines for this query, and the user's subsequent clicks, the authors conclude that the goal of this search was "undirected" information.
  • 14. Applications and limitations ● "If our findings about ● "One issue is that we the relatively small have no way of knowing number of navigational conclusively whether the queries are accurate, goal we inferred for a they suggest that much query is in fact the user’s of the attention in the actual goal. In the future, commercial search we would like to combine engine world may be our work with user misdirected" (18). studies, including qualitative data such as diary reports of user goals" (19).
  • 15. Questions ● How does Rose and Levinson's user goals paradigm compare to the idea of document relevance? Does a result need to meet a search goal in order for the user to perceive it as relevant, or simply convey information related to the search query? ● In what ways does their user goals model take into account query refinement and/or query expansion? ● What is the user's goal in the Aloha/American Airlines scenario (outlined on slides 8 and 9)? ● How would you apply Rose and Levinson's research to the following questions: ○ Why does a search occur? ○ What is its purpose? ○ How does it proceed?