23. Juli 2010

1. Collaborative Web By Benjamin Riedel 23. Juli 2010 1

2. 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 Riedel 2

3. 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 Riedel 3

4. 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 Riedel 4

5. 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%) Most frequent engaged collaborative activities Benjamin Riedel 5

6. Always Remember: The Study was conducted in the year 2006 – the numbers probably changed by now – a lot. Frequency (only those 53,4% who knew they are searching collaboratly) Benjamin Riedel 6

7. Searching tasks people collaborate on Benjamin Riedel 7

8. 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 Riedel 8

9. 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 Riedel 9

10. 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 Riedel 10

11. 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 Riedel 11

12. 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 Riedel 12

13. Example Benjamin Riedel 13

14. 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 Riedel 14

15. 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 Riedel 15

16. 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 Riedel 16

17. 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 0.25 + 0.2 + 0.25 = 0.7 Benjamin Riedel 17

18. How to present a social summary? Benjamin Riedel 18

19. Discussion Benjamin Riedel 19

20. 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.