23. Juli By Benjamin Riedel Collaborative Web

Benjamin Riedel2 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

Benjamin Riedel3 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

Benjamin Riedel4 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!

Benjamin Riedel5 Most frequent engaged collaborative activities Watched over someone ‟ s shoulder as he/she searched the Web, and suggested alternate query terms. (87,7%) ed 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%) ed 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%)

Benjamin Riedel6 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 searchRespondants Daily0,9% Weekly25,7% Monthly48,6% Yearly24,8%

Benjamin Riedel7 Searching tasks people collaborate on TaskRespondants Travel planning27,5% General shopping tasks25,7% Literature search20,2% Technical information16,5% Fact finding16,5% Social planning12,8% Medical information6,4% Real estate6,4%

Benjamin Riedel8 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

Benjamin Riedel9 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

Benjamin Riedel10 Social summarization in collaborative web search (Oisín Boydell and Barry Smyth, 2009) Improving snippets by using context provided by community of like-minded searchers

Benjamin Riedel11 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

Benjamin Riedel12 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

Benjamin Riedel13 Example

Benjamin Riedel14 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)

Benjamin Riedel15 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

Benjamin Riedel16 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..“

Benjamin Riedel17 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 3 Now 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 = 0.7

Benjamin Riedel18 How to present a social summary?

Benjamin Riedel19 Discussion

References Morris MR. A survey of collaborative web search practices. Conference on Human Factors in Computing Systems. 2008: Boydell O, Smyth B. Social summarization in collaborative web search. Information Processing & Management. 2010;46(6):