1. 1 Enabling web browsers to augment web sites’ filtering and sorting functionalities David Huynh · Rob Miller · David Karger MIT Computer Science & Artificial Intelligence Laboratory UIST 2006 · Montreux, Switzerland

2. 2 Automatic web content scraping (2003 ― now) 1.Zhai, Y., and B. Liu. Web data extraction based on partial tree alignment. WWW 2005. 2.Hogue, A. and D. Karger. Thresher: automating the unwrapping of semantic content from the World Wide Web. WWW 2005. 3.Reis, D.C., P.B. Golgher, A.S. Silva, and A.F. Laender. Automatic Web news extraction using tree edit distance. WWW 2004. 4.Lerman, K., L. Getoor, S. Minton, and C. Knoblock. Using the structure of Web sites for automatic segmentation of tables. SIGMOD 2004. 5.Ramaswamy, L., et. al. Automatic detection of fragments in dynamically generated web pages. WWW 2004. 6.Wang, J.-Y., and F. Lochovsky. Data extraction and label assignment for Web databases. WWW 2003. 7.Arasu, A. and H. Garcia-Molina. Extracting structured data from Web pages. SIGMOD 2003. 8.Liu, B., R. Grossman, and Y. Zhai. Mining data records in Web pages. SIGKDD 2003.

3. 3 … but no one has tried to put … Automatic structured web content scraping technologies in the hands of end-users

4. 4 … let’s run through a real task … Paperback books published in 2005 or later by John Grisham on Amazon

5. 5 … that was a demo of putting … Automatic structured web content scraping technologies in the hands of end-users

6. 6 Sifter browser extension

7. 7 Outline Motivations 1.User Interface Design Extraction Augmentation 2.Extraction Algorithm Evaluations 1.Extraction Algorithm 2.User Interface Design Conclusions

8. 8 Motivations Not all web sites are designed based on task analysis and user analysis. Faceted browsing? Maps view? Calendar view? Features are not implemented consistently across sites. Web browsers can provide a unified sorting/filtering interface. Not all users have exactly the same needs. No site can ever design for all users. Each web browser can tailor experience to its owner.

9. 9 Motivations

10. 10 Outline Motivations 1.User Interface Design Extraction Augmentation 2.Extraction Algorithm Evaluations 1.Extraction Algorithm 2.User Interface Design Conclusions

11. 11 User Interface Design – Extraction Web content extraction is a system precondition poorly understood by users. If it doesn’t let me do this,… If the web site understands that this is the original price ( $8.99 ),… If I can see that this is a date (“last Christmas”),…

12. 12 User Interface Design – Extraction Extraction is lengthy and error-prone. We explore UI potentials even in the face of fragile extraction. This lets us know which aspects of extraction should be improved first, and in which ways. We minimize the steps required to kick-start extraction. But we give the user an chance to make correction early.

13. 13 UI Design - Extraction 1 st click preview of results controls for making correction 2 nd click if all goes well

14. 14 Outline Motivations 1.User Interface Design Extraction Augmentation 2.Extraction Algorithm Evaluations 1.Extraction Algorithm 2.User Interface Design Conclusions

15. 15 User Interface Design - Augmentation Novelty Presentation of data remains unchanged … except for a few asterisks. Presentation might be well-designed with domain specific knowledge, and worth to keep as-is. Semantics of the data are in the presentation. We want to maintain visual context. Filtering and sorting are supported without resorting to field names.

16. 16 User Interface Design - Augmentation By keeping the original visual presentation of the data, and then applying automatic content extraction technology, we can provide additional functionalities without needing, trying, or pretending to understand the semantics of the data. format? binding? medium? who cares?!

17. 17 … ssshhhh … Semantics is Overrated

18. 18 Outline Motivations 1.User Interface Design Extraction Augmentation 2.Extraction Algorithm Evaluations 1.Extraction Algorithm 2.User Interface Design Conclusions

19. 19 Extraction Algorithm Detection of 1.Items of interest 2.Subsequent pages 3.Fields within items

20. 20 1.Items occupy most of the page area. 2.Each item contains links. Find THE set of similar links whose outer containers occupy the largest page area compared to other sets of links. Extraction Algorithm - Assumptions

21. 21 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2

22. 22 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 A

23. 23 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 DIV/A

24. 24 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 TD/DIV/A

25. 25 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 TR/TD/DIV/A

26. 26 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 TABLE/TR/TD/DIV/A

27. 27 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A

28. 28 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A Found similar links!

29. 29 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A/..

30. 30 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A/../..

31. 31 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A/../../..

32. 32 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A/../../../..

33. 33 BODY TABLE TR - item 1 DIV A TD TR - item 2 DIV A TD BODY TABLE TR TD DIV A TR TD DIV A Item 1Item 2 BODY/TABLE/TR/TD/DIV/A/../../.. Found one potential set of items!

34. 34 Extraction Algorithm – Subsequent page detection

35. 35 Extraction Algorithm – Subsequent page detection URL parameters http://amazon.com/... ?... &page=2&... http://amazon.com/... ?... &page=3&... http://amazon.com/... ?... &page=4&...

36. 36 Outline Motivations 1.User Interface Design Extraction Augmentation 2.Extraction Algorithm Evaluations 1.Extraction Algorithm 2.User Interface Design Conclusions

37. 37 Evaluations – Extraction algorithm Test conducted over 30 web sites: Amazon, BestBuy, CNET Reviews, Froogle, Target, Walmart, … Item detection Items on 27 / 30 collections can be identified by xpaths (in the remaining 3, items consist of sibling/cousin nodes) … but only 24 / 27 were automatically detected Subsequent page detection For 22 / 27 collections, subsequent pages could be identified. For 19 / 22 collections, original numbers of items were recovered. Overall 19 / 30 = 63% accuracy We measure accuracy at the level of whole collections, not individual items.

38. 38 Evaluations – User Interface Design Extraction algorithm is still fragile Formative evaluation of UI Is “web content extraction” too high a conceptual barrier? Is in-place sorting/filtering augmentation usable? No field name – usable? Is such augmentation useful?

39. 39

40. 40 Evaluations – User Interface Design Task 1: Structured This task lets subjects get familiar with the UI. No specific help or tutorial is provided. Subject follows a sequence of high-level instructions to ultimately perform a complex query. sort by price filter by date Subject is given 5 min to perform a similar query using the web site. Task 2: Unstructured Subject judges whether a sale of several products is good.

41. 41 Evaluations – User Interface Design Task 1: Structured 8/8 subjects completed the task using our system. 5/8 … using the web site within 5 minutes. 1/8 knew about Amazon’s Advanced Search. All subjects were familiar with Amazon. A unified filtering/sorting UI can be more usable than different UIs on different sites. Task 2: Unstructured 7/8 subjects completed the task using our system. 1 refused to complete the task.

42. 42 Evaluations – UI Design Survey responses indicate Our system is usable and useful … while it offers advanced functionalities.

43. 43 Conclusions In our work, we … Preserve original presentation to leverage the semantics within it; Provide filter/sort functionalities without field names; Put automatic web content extraction technologies into the hands of end-users; Show evidence that it’s usable and useful. For future work, we will focus on … Error recovery; Merging data from several sites.

44. 44 More information http://simile.mit.edu/wiki2/Sifter Firefox extension installation file Open source code + build instructions Links to video and user study data