slides_chap02.pdf

5
slides_chap02.pdf


slides_chap02.pdf

slides_chap02.pdf

  • 1. Modern Information Retrieval
    Chapter
    2
    User Interfaces for Search
    How People Search
    Search Interfaces Recently
    Visualization in Search Interfaces
    Design and Evaluation of Search Interfaces
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 1
  • 2. Introduction
    This chapter focuses
    on
    the human users of search systems
    the search user interface, i.e., the window through which search
    systems are seen
    The user interface role is to aid in the searchers’
    understanding and expression of their information need
    Further, the interface should help users
    formulate their queries
    select among available information sources
    understand search results
    keep track of the progress of their search
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 2
  • 3. How People Search
    Chap
    02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 3
  • 4. How People Search
    User
    interaction with search interfaces differs
    depending on
    the type of task
    the domain expertise of the information seeker
    the amount of time and effort available to invest in the process
    Marchionini makes a distinction between information
    lookup and exploratory search
    Information lookup tasks
    are akin to fact retrieval or question answering
    can be satisfied by discrete pieces of information: numbers,
    dates, names, or Web sites
    can work well for standard Web search interactions
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 4
  • 5. How People Search
    Exploratory
    search is divided into learning and
    investigating tasks
    Learning search
    requires more than single query-response pairs
    requires the searcher to spend time
    scanning and reading multiple information items
    synthesizing content to form new understanding
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 5
  • 6. How People Search
    Investigating
    refers to a longer-term process which
    involves multiple iterations that take place over perhaps very long
    periods of time
    may return results that are critically assessed before being
    integrated into personal and professional knowledge bases
    may be concerned with finding a large proportion of the relevant
    information available
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 6
  • 7. How People Search
    Information
    seeking can be seen as being part of a
    larger process referred to as sensemaking
    Sensemaking is an iterative process of formulating a
    conceptual representation from a large collection
    Russell et al. observe that most of the effort in
    sensemaking goes towards the synthesis of a good
    representation
    Some sensemaking activities interweave search
    throughout, while others consist of doing a batch of
    search followed by a batch of analysis and synthesis
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 7
  • 8. How People Search
    Examples
    of deep analysis tasks that require
    sensemaking (in addition to search)
    the legal discovery process
    epidemiology (disease tracking)
    studying customer complaints to improve service
    obtaining business intelligence.
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 8
  • 9. Classic × Dynamic Model
    Classic notion of the information seeking process:
    1. problem identification
    2. articulation of information need(s)
    3. query formulation
    4. results evaluation
    More recent models emphasize the dynamic nature of
    the search process
    The users learn as they search
    Their information needs adjust as they see retrieval results and
    other document surrogates
    This dynamic process is sometimes referred to as the
    berry picking model of search
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 9
  • 10. Classic × Dynamic Model
    The rapid response times of today’s Web search
    engines allow searchers:
    to look at the results that come back
    to reformulate their query based on these results
    This kind of behavior is a commonly-observed strategy
    within the berry-picking approach
    Sometimes it is referred to as orienteering
    Jansen et al made a analysis of search logs and found
    that the proportion of users who modified queries is
    52%
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 10
  • 11. Classic × Dynamic Model
    Some seeking models cast the process in terms of
    strategies and how choices for next steps are made
    In some cases, these models are meant to reflect conscious
    planning behavior by expert searchers
    In others, the models are meant to capture the less planned,
    potentially more reactive behavior of a typical information seeker
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 11
  • 12. Navigation × Search
    Navigation:
    the searcher looks at an information
    structure and browses among the available information
    This browsing strategy is preferrable when the
    information structure is well-matched to the user’s
    information need
    it is mentally less taxing to recognize a piece of information than it
    is to recall it
    it works well only so long as appropriate links are available
    If the links are not avaliable, then the browsing
    experience might be frustrating
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 12
  • 13. Navigation × Search
    Spool
    discusses an example of a user looking for a
    software driver for a particular laser printer
    Say the user first clicks on printers, then laser printers,
    then the following sequence of links:
    HP laser printers
    HP laser printers model 9750
    software for HP laser printers model 9750
    software drivers for HP laser printers model 9750
    software drivers for HP laser printers model 9750 for the
    Win98 operating system
    This kind of interaction is acceptable when each
    refinement makes sense for the task at hand
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 13
  • 14. Search Process
    Numerous studies
    have been made of people engaged
    in the search process
    The results of these studies can help guide the design
    of search interfaces
    One common observation is that users often
    reformulate their queries with slight modifications
    Another is that searchers often search for information
    that they have previously accessed
    The users’ search strategies differ when searching over
    previously seen materials
    Researchers have developed search interfaces support
    both query history and revisitation
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 14
  • 15. Search Process
    Studies also
    show that it is difficult for people to
    determine whether or not a document is relevant to a
    topic
    The less users know about a topic, the poorer judges they are of
    whether a search result is relevant to that topic
    Other studies found that searchers tend to look at only
    the top-ranked retrieved results
    Further, they are biased towards thinking the top one or
    two results are better than those beneath them
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 15
  • 16. Search Process
    Studies also
    show that people are poor at estimating
    how much of the relevant material they have found
    Other studies have assessed the effects of knowledge
    of the search process itself
    These studies have observed that experts use different
    strategies than novices searchers
    For instance, Tabatabai et al found that
    expert searchers were more patient than novices
    this positive attitude led to better search outcomes
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 16
  • 17. Search Interfaces Recently
    Chap
    02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 17
  • 18. Getting Started
    How does
    an information seeking session begin in
    online information systems?
    The most common way is to use a Web search engine
    Another method is to select a Web site from a personal
    collection of already-visited sites
    which are typically stored in a browser’s bookmark
    Online bookmark systems are popular among a smaller segment
    of users
    Ex: Delicious.com
    Web directories are also used as a common starting point, but
    have been largely replaced by search engines
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 18
  • 19. Query Specification
    The primary
    methods for a searcher to express their
    information need are either
    entering words into a search entry form
    selecting links from a directory or other information organization
    display
    For Web search engines, the query is specified in
    textual form
    Typically, Web queries today are very short consisting
    of one to three words
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 19
  • 20. Query Specification
    Short queries
    reflect the standard usage scenario in
    which the user tests the waters:
    If the results do not look relevant, then the user reformulates their
    query
    If the results are promising, then the user navigates to the most
    relevant-looking Web site
    This search behavior is a demonstration of the
    orienteering strategy of Web search
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 20
  • 21. Query Specification
    Before the
    Web, search systems regularly supported
    Boolean operators and command-based syntax
    However, these are often difficult for most users to understand
    Jansen et al conducted a study over a Web log with
    1.5M queries, and found that
    2.1% of the queries contained Boolean operators
    7.6% contained other query syntax, primarily double-quotation
    marks for phrases
    White et al examined interaction logs of nearly 600,000
    users, and found that
    1.1% of the queries contained one or more operators
    8.7% of the users used an operator at any time
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 21
  • 22. Query Specification
    Web ranking
    has gone through three major phases
    In the first phase, from approximately 1994–2000:
    Since the Web was much smaller then, complex queries were
    less likely to yield relevant information
    Further, pages retrieved not necessarily contained all query
    words
    Around 1997, Google moved to conjunctive queries only
    The other Web search engines followed, and conjunctive ranking
    became the norm
    Google also added term proximity information and page
    importance scoring (PageRank)
    As the Web grew, longer queries posed as phrases started to
    produce highly relevant results
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 22
  • 23. Query Specification Interfaces
    The
    standard interface for a textual query is a search
    box entry form
    Studies suggest a relationship between query length
    and the width of the entry form
    Results found that either small forms discourage long queries or
    wide forms encourage longer queries
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 23
  • 24. Query Specification Interfaces
    Some
    entry forms are followed by a form that filters the
    query in some way
    For instance, at yelp.com, the user can refine the
    search by location using a second form
    Notice that the yelp.com form also shows the user’s
    home location, if it has been specified previously
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 24
  • 25. Query Specification Interfaces
    Some
    search forms show hints on what kind of
    information should be entered into each form
    For instance, in zvents.com search, the first box is
    labeled “what are you looking for”?
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 25
  • 26. Query Specification Interfaces
    The
    previous example also illustrates specialized input
    types that some search engines are supporting today
    The zvents.com site recognizes that words like “tomorrow” are
    time-sensitive
    It also allows flexibility in the syntax of dates
    To illustrate, searching for “comedy on wed”
    automatically computes the date for the nearest future
    Wednesday
    This is an example of how the interface can be designed to reflect
    how people think
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 26
  • 27. Query Specification Interfaces
    Some
    interfaces show a list of query suggestions as the
    user types the query
    This is referred to as auto-complete, auto-suggest, or dynamic
    query suggestions
    Anick et al found that users clicked on dynamic Yahoo
    suggestions one third of the time
    Often the suggestions shown are those whose prefix
    matches the characters typed so far
    However, in some cases, suggestions are shown that only have
    interior letters matching
    Further, suggestions may be shown that are synonyms
    of the words typed so far
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 27
  • 28. Query Specification Interfaces
    Dynamic
    query suggestions, from Netflix.com
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 28
  • 29. Query Specification Interfaces
    The
    dynamic query suggestions can be derived from
    several sources, including:
    The user’s own query history
    A set of metadata that a Web site’s designer considers important
    All of the text contained within a Web site
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 29
  • 30. Query Specification Interfaces
    Dynamic
    query suggestions, grouped by type, from
    NextBio.com:
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 30
  • 31. Retrieval Results Display
    When
    displaying search results, either
    the documents must be shown in full, or else
    the searcher must be presented with some kind of representation
    of the content of those documents
    The document surrogate refers to the information that
    summarizes the document
    This information is a key part of the success of the search
    interface
    The design of document surrogates is an active area of research
    and experimentation
    The quality of the surrogate can greatly effect the perceived
    relevance of the search results listing
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 31
  • 32. Retrieval Results Display
    In
    Web search, the page title is usually shown
    prominently, along with the URL and other metadata
    In search over information collections, metadata such
    as date published and author are often displayed
    Text summary (or snippet) containing text extracted
    from the document is also critical
    Currently, the standard results display is a vertical list of
    textual summaries
    This list is sometimes referred to as the SERP (Search
    Engine Results Page)
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 32
  • 33. Retrieval Results Display
    In
    some cases the summaries are excerpts drawn from
    the full text that contain the query terms
    In other cases, specialized kinds of metadata are
    shown in addition to standard textual results
    This technique is known as blended results or universal search
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 33
  • 34. Retrieval Results Display
    For
    example, a query on a term like “rainbow” may
    return sample images as one entry in the results listing
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 34
  • 35. Retrieval Results Display
    A
    query on the name of a sports team might retrieve the
    latest game scores and a link to buy tickets
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 35
  • 36. Retrieval Results Display
    Nielsen
    notes that in some cases the information need
    is satisfied directly in the search results listing
    This makes the search engine an “answer engine”
    Displaying the query terms in the context in which they
    appear in the document:
    Improves the user’s ability to gauge the relevance of the results
    It is sometimes referred to as KWIC – keywords in context
    It is also known as query-biased summaries, query-oriented
    summaries, or user-directed summaries
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 36
  • 37. Retrieval Results Display
    The
    visual effect of query term highlighting can also
    improve usability of search results listings
    Highlighting can be shown both in document surrogates in the
    retrieval results and in the retrieved documents
    Determining which text to place in the summary, and
    how much text to show, is a challenging problem
    Often the summaries contain all the query terms in
    close proximity to one another
    However, there is a trade-off between
    Showing contiguous sentences, to aid in coherence in the result
    Showing sentences that contain the query terms
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 37
  • 38. Retrieval Results Display
    Some
    results suggest that it is better to show full
    sentences rather than cut them off
    On the other hand, very long sentences are usually not desirable
    in the results listing
    Further, the kind of information to display should vary
    according to the intent of the query
    Longer results are deemed better than shorter ones for certain
    types of information need
    On the other hand, abbreviated listing is preferable for
    navegational queries
    Similarly, requests for factual information can be satisfied with a
    concise results display
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 38
  • 39. Retrieval Results Display
    Other
    kinds of document information can be usefully
    shown in the search results page
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 39
  • 40. Retrieval Results Display
    The
    page results below show figures extracted from
    journal articles alongside the search results
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 40
  • 41. Query Reformulation
    There are
    tools to help users reformulate their query
    One technique consists of showing terms related to the query or
    to the documents retrieved in response to the query
    A special case of this is spelling corrections or
    suggestions
    Usually only one suggested alternative is shown: clicking on that
    alternative re-executes the query
    In years back, the search results were shown using the
    purportedly incorrect spelling
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 41
  • 42. Query Reformulation
    Microsoft Live’s
    search results page for the query “IMF”
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 42
  • 43. Query Reformulation
    Term expansion:
    search interfaces are increasingly
    employing related term suggestions
    Log studies suggest that term suggestions are a
    somewhat heavily-used feature in Web search
    Jansen et al made a log study and found that 8% of
    queries were generated from term suggestions
    Anick et al found that 6% of users who were exposed to
    term suggestions chose to click on them
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 43
  • 44. Query Reformulation
    Some query
    term suggestions are based on the entire
    search session of the particular user
    Others are based on behavior of other users who have
    issued the same or similar queries in the past
    One strategy is to show similar queries by other users
    Another is to extract terms from documents that have been
    clicked on in the past by searchers who issued the same query
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 44
  • 45. Query Reformulation
    Relevance feedback
    is another method whose goal is
    to aid in query reformulation
    The main idea is to have the user indicate which
    documents are relevant to their query
    In some variations, users also indicate which terms extracted
    from those documents are relevant
    The system then computes a new query from this
    information and shows a new retrieval set
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 45
  • 46. Query Reformulation
    Nonetheless, this
    method has not been found to be
    successful from a usability perspective
    Because that, it does not appear in standard interfaces today
    This stems from several factors:
    People are not particularly good at judging document relevance,
    especially for topics with which they are unfamiliar
    The beneficial behavior of relevance feedback is inconsistent
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 46
  • 47. Organizing Search Results
    Organizing
    results into meaningful groups can help
    users understand the results and decide what to do next
    Popular methods for grouping search results: category
    systems and clustering
    Category system: meaningful labels organized in such
    a way as to reflect the concepts relevant to a domain
    Good category systems have the characteristics of being
    coherent and relatively complete
    Their structure is predictable and consistent across search
    results for an information collection
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 47
  • 48. Organizing Search Results
    The
    most commonly used category structures are flat,
    hierarchical, and faceted categories
    Flat categories are simply lists of topics or subjects
    They can be used for grouping, filtering (narrowing), and sorting
    sets of documents in search interfaces
    Most Web sites organize their information into general
    categories
    Selecting that category narrows the set of information shown
    accordingly
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 48
  • 49. Organizing Search Results
    Some
    experimental Web search engines automatically
    organize results into flat categories
    Studies using this kind of design have received positive user
    responses (Dumais et al, Kules et al)
    However, it can difficult to find the right subset of
    categories to use for the vast content of the Web
    Rather, category systems seem to work better for more
    focused information collections
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 49
  • 50. Organizing Search Results
    In
    the early days of the Web, hierarchical directory
    systems such as Yahoo’s were popular
    Hierarchy can also be effective in the presentation of
    search results over a book or other small collection
    The Superbook system was an early search interface
    based on this idea
    In the Superbook system, the search results were
    shown in the context of the table-of-contents hierarchy
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 50
  • 51. Organizing Search Results
    The
    SuperBook interface for showing retrieval results in
    context
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 51
  • 52. Organizing Search Results
    An
    alternative representation is the faceted metadata
    Unlike flat categories, faceted metadata allow the
    assignment of multiple categories to a single item
    Each category corresponds to a different facet
    (dimension or feature type) of the collection of items
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 52
  • 53. Organizing Search Results
    Figure
    below shows a example of faceted navigation
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 53
  • 54. Organizing Search Results
    Clustering
    refers to the grouping of items according to
    some measure of similarity
    It groups together documents that are similar to one
    another but different from the rest of the collection
    Such as all the document written in Japanese that appear in a
    collection of primarily English articles
    The greatest advantage of clustering is that it is fully
    automatable
    The disadvantages of clustering include
    an unpredictability in the form and quality of results
    the difficulty of labeling the groups
    the counter-intuitiveness of cluster sub-hierarchies
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 54
  • 55. Organizing Search Results
    Output
    produced using Findex clustering
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 55
  • 56. Organizing Search Results
    Cluster
    output on the query “senate”, from Clusty.com
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 56
  • 57. Visualization in Search Interfaces
    Experimentation with visualization for search has been
    primarily applied in the following ways:
    Visualizing Boolean syntax
    Visualizing query terms within retrieval results
    Visualizing relationships among words and documents
    Visualization for text mining
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 57
  • 58. Visualizing Boolean Syntax
    Boolean
    query syntax is difficult for most users and is
    rarely used in Web search
    For several years, researchers have experimented with
    how to visualize Boolean query specification
    A common approach is to show Venn diagrams
    A more flexible version of this idea was seen in the
    VQuery system, proposed by Steve Jones
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 58
  • 59. Visualizing Boolean Syntax
    The
    VQuery interface for Boolean query specification
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 59
  • 60. Visualizing Query Terms
    Understanding
    the role of the query terms within the
    retrieved docs can help relevance assessment
    Experimental visualizations have been designed that
    make this role more explicit
    In the TileBars interface, for instance, documents are
    shown as horizontal glyphs
    The locations of the query term hits marked along the
    glyph
    The user is encouraged to break the query into its
    different facets, with one concept per line
    Then, the lines show the frequency of occurrence of
    query terms within each topic
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 60
  • 61. Visualizing Query Terms
    The
    TileBars interface
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 61
  • 62. Visualizing Query Terms
    Other
    approaches include placing the query terms in
    bar charts, scatter plots, and tables
    A usability study by Reiterer et al compared five views:
    a standard Web search engine-style results listing
    a list view showing titles, document metadata, and a graphic
    showing locations of query terms
    a color TileBars-like view
    a color bar chart view like that of Veerasamy & Belkin
    a scatter plot view plotting relevance scores against date of
    publication
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 62
  • 64. Visualizing Query Terms
    Colored
    TileBars view
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 64
  • 65. Visualizing Query Terms
    When
    asked for subjective responses, the 40
    participants of the study preferred, on average, in this
    order:
    Field-sortable view first
    TileBars
    Web-style listing
    The bar chart and scatter plot received negative
    responses
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 65
  • 66. Visualizing Query Terms
    Another
    variation on the idea of showing query term hits
    within documents is to show thumbnails
    Thumbnails are miniaturized rendered versions of the visual
    appearance of the document
    However, Czerwinski et al found that thumbnails are no
    better than blank squares for improving search results
    The negative study results may stem from a problem
    with the size of the thumbnails
    Woodruff et al shows that making the query terms more visible
    via highlighting within the thumbnail improves its usability
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 66
  • 67. Visualizing Query Terms
    Textually
    enhanced thumbnails
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 67
  • 68. Words and Docs Relationships
    Numerous works proposed variations on the idea of
    placing words and docs on a two-dimensional canvas
    In these works, proximity of glyphs represents semantic
    relationships among the terms or documents
    An early version of this idea is the VIBE interface
    Documents containing combinations of the query terms are
    placed midway between the icons representing those terms
    The Aduna Autofocus and the Lyberworld projects
    presented a 3D version of the ideas behind VIBE
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 68
  • 69. Words and Docs Relationships
    The VIBE display
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 69
  • 70. Words and Docs Relationships
    Another idea is to map docs or words from a very high-
    dimensional term space down into a 2D plane
    The docs or words fall within that plane, using 2D or 3D
    This variation on clustering can be done to
    documents retrieved as a result of a query
    documents that match a query can be highlighted within a
    pre-processed set of documents
    InfoSky and xFIND’s VisIslands are two variations on
    these starfield displays
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 70
  • 71. Words and Docs Relationships
    InfoSky, from Jonker et al
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 71
  • 72. Words and Docs Relationships
    xFIND’s VisIslands, from Andrews et al
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 72
  • 73. Words and Docs Relationships
    These views are relatively easy to compute and can be
    visually striking
    However, evaluations that have been conducted so far
    provide negative evidence as to their usefulness
    The main problems are that the contents of the documents are
    not visible in such views
    A more promising application of this kind of idea is in
    the layout of thesaurus terms, in a small network graph
    Ex: Visual Wordnet
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 73
  • 74. Words and Docs Relationships
    The Visual Wordnet view of the WordNet lexical
    thesaurus
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 74
  • 75. Visualization for Text Mining
    Visualization is also used for purposes of analysis and
    exploration of textual data
    Visualizations such as the Word Tree show a piece of a
    text concordance
    It allows the user to view which words and phrases commonly
    precede or follow a given word
    Another example is the NameVoyager, which shows
    frequencies of names for United States. children across time
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 75
  • 76. Visualization for Text Mining
    The Word Tree visualization, on Martin Luther King’s
    I have a dream speech, from Wattenberg et al
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 76
  • 77. Visualization for Text Mining
    The popularity of baby names over time (names
    beginning with JA), from babynamewizard.com
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 77
  • 78. Visualization for Text Mining
    Visualization is also used in search interfaces intended
    for analysts
    An example is the TRIST information triage system,
    from Proulx et al
    In this system, search results is represented as
    document icons
    Thousands of documents can be viewed in one display
    It supports multiple linked dimensions that allow for
    finding characteristics and correlations among the docs
    Its designers won the IEEE Visual Analytics Science
    and Technology (VAST) contest for two years running
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 78
  • 79. Visualization for Text Mining
    The TRIST interface with results for queries related to
    Avian Flu
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 79
  • 80. Design and Evaluation
    User
    interface design: a field of Human-Computer
    Interaction (HCI)
    This field studies how people think about, respond to,
    and use technology
    User-centered design: a set of practices developed to
    facilitate the design of interfaces
    The design process begins by determining what the
    intended users’ goals are
    Then, the interface is devised to help people achieve
    those goals by completing a series of tasks
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 80
  • 81. Design and Evaluation
    Goals
    in the domain of information access can range
    quite widely
    From finding a plumber to keeping informed about a business
    competitor
    From writing a publishable scholarly article to investigating an
    allegation of fraud
    The design of interfaces is an iterative process, in which
    the goals and tasks are elucidated via user research
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 81
  • 82. Design and Evaluation
    Evaluating
    a user interface is often different from
    evaluating a ranking algorithm or a crawling technique
    A crawler can be assessed by crisp quantitative metrics such as
    coverage and freshness
    A ranking algorithm can be evaluated by precision, recall, and
    speed
    The quality of a user interface is determined by how
    people respond to it
    Subjective responses are as, if not more, important
    than quantitative measures
    If a person has a choice between two systems, they will
    use the one they prefer
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 82
  • 83. Design and Evaluation
    The
    reasons for preference may be determined by a
    host of factors:
    Speed, familiarity, aesthetics, preferred features, or perceived
    ranking accuracy
    Often the preferred choice is the familiar one
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 83
  • 84. Design and Evaluation
    How
    best to evaluate a user interface depends on the
    current stage in the development cycle
    When starting with a new design or idea, discount
    usability methods are typically used
    Example: showing a few users different designs asking them to
    indicate which parts are promising and which are not
    Another commonly used discount evaluation method is
    heuristic evaluation
    Usability experts “walk through” a design and evaluate the
    functionality in accordance with a set of design guidelines
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 84
  • 85. Design and Evaluation
    A
    formal experiment must be carefully designed to take
    into account potentially confounding factors
    For instance, it is important for participants to be motivated to do
    well on the task
    This kind of study can uncover important subjective
    results
    Such as whether a new design is strongly preferred over a
    baseline
    However, it is difficult to find accurate quantitative
    differences with a small number of participants
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 85
  • 86. Design and Evaluation
    Another
    problem: the timing variable is not the right
    measure for evaluating an interactive search session
    A tool that allows the searcher to learn about their subject matter
    as they search may be more beneficial, but take more time
    Two approaches to evaluating search interfaces have
    gained in popularity in recent years
    One is to conduct a longitudinal study
    Participants use a new interface for an extended period of time,
    and their usage is monitored and logged
    Evaluation is based both on log analysis and questionnaires and
    interviews with the participants
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 86
  • 87. Design and Evaluation
    Another
    evaluation technique is to perform experiments
    on already heavily-used Web sites
    Consider a search engine that receives millions of
    queries a day
    a randomly selected subset of the users is shown a new design
    their actions are logged and compared to another randomly
    selected control group that continues to use the existing interface
    this approach is often referred to as bucket testing, A/B testing
    Chap 02: User Interfaces for Search, Baeza-Yates & Ribeiro-Neto, Modern Information Retrieval, 2nd Edition – p. 87


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