23. Juli 2010 1

By Benjamin Riedel

Collaborative Web

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Outline

• A Survey of Collaborative Web Search Practices (Meredith Ringel Morris, 2006)

• Social summarization in collaborative web search (Oisín Boydell and Barry Smyth, 2009)

• Discussion

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A Survey of Collaborative Web Search Practices (2006)

• Survey of the using of collaborative web search

• Demographics: 204 knowledge workers at a technology company 80,4% male 21 to 61 years old (median 36) 38% researchers, 22% software developers, 17% program managers 73,5% see themselves as web searching experts

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What counts as Collaborative Web Search?

• Term is not clear:

While only 53,4% of participients say they ever cooperated to search the web, only 2,9% have not used any of the collaborative search activities listed in the study

People do collaborate when searching, but they are not aware of it!

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Most frequent engaged collaborative activities

Watched over someone‟s shoulder as he/she searched the Web, and suggested alternate query terms. (87,7%)

E-mailed someone links to share the results of a Web search. (86,3%)

Showed a personal display to other people to share the results of a Web search. (85,3%)

E-mailed someone a textual summary to share the results of a Web search. (60,3%)

Called someone on the phone to tell them about the results of a Web search (49%)

Printed Web pages on paper to share the results of a Web search. (41,2%)

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Frequency (only those 53,4% who knew they are searching collaboratly)

Always Remember: The Study was conducted in the year 2006 – the numbers probably changed by now – a lot.

Frequency of collaborting on Web search Respondants

Daily 0,9%

Weekly 25,7%

Monthly 48,6%

Yearly 24,8%

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Searching tasks people collaborate on

Task Respondants

Travel planning 27,5%

General shopping tasks 25,7%

Literature search 20,2%

Technical information 16,5%

Fact finding 16,5%

Social planning 12,8%

Medical information 6,4%

Real estate 6,4%

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Types of collaboration

Brute Force: All participants just search and share their results afterwards

Divide-and-conquer:Sharing out sub-topics or places to search to everyone beforehand, so they will find different relevant results

Race:Trying to find something before everyone else does

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Obstacles

No way to be sure that you are following a trail no one else of your group followed before

Difficult to share results:When communicating only per voice: URLs too long to dictate, hard to navigate people to your findings

Realizing the need to share the findings not until you can't find them anymore:Browser history not very helpful when looking for a specific site in a search

No UI that helps you to teach people how to search

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Social summarization in collaborative web search (Oisín Boydell and Barry Smyth, 2009)

• Improving snippets by using context provided by community of like-minded searchers

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Basic Idea

• Like-minded people will probably find similar pieces of a text interesting and therefore need similar snippets.

• So searches have to be compared to searches that were helpful for similar people in the past

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Basic Idea

• Like-minded people will probably find similar pieces of a text interesting and therefore need similar snippets.

• So searches have to be compared to searches that were helpful for similar people in the past

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Example

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How to achieve this?

• 1. Create surrogates (Sc) for every document (r) in a search (q) that gets a hit and save the used snippet s(r, q)

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How to achieve this?

• 2. Sort and replace snippet fragments in each Surrogate, so that there is only one homogenous representation of each fragment

Therefore: Find overlapping fragments Search those for fragment pairs where the overlap is greater than a threshold

Replace the shorter fragment of the pair with the longer one

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How to achieve this?

• 3. Rank the fragments by counting in how many snippets they occur

• 4. Create a social summary for a document by putting together all fragments of its surrogate, sorted by their rank

Example: If there are 3 Fragments: „eating a lot pizza“ (Rank 3), „using computers“ (Rank 5), „commenly referenced to as nerds“ (Rank 2) there social summary would by:

„...using computers … eating a lot pizza … commenly referenced to as nerds ..“

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Problem: We created only a generel social summary, but we want to have one specific to each query

Answer: Weighting the rank of each fragment by the compliance between the used query and the current query

Example: fragment „eating Pizza“ can be found in the snippets for three different querys:

„what do nerds do“„what should I eat today“„how to eat pizza“By just counting the snippets the rank would have been 3Now we look at our query: „what pizza is best“:

25% of the first fragment equal our query, so we take 0.25 points for that, 0.2 for the second and 0.25 for the third. Thus the rank of „eating Pizza“ for this query is 0.25 + 0.2 + 0.25 = 0.7

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How to present a social summary?

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Discussion

References

Morris MR. A survey of collaborative web search practices. Conference on Human Factors in Computing Systems. 2008:1657-1660

Boydell O, Smyth B. Social summarization in collaborative web search. Information Processing & Management. 2010;46(6):782-798.