Content Analysis Techniques to Ease Browsing with Handhelds

1. Content Analysis Techniques to Ease Browsing with Handhelds Jalal Mahmud Yevgen Borodin I.V. Ramakrishnan Department of Computer Science State University of New York at Stony Brook Stony Brook, NY 11794

2. Outline • Browsing with Handhelds: • Content Analysis Techniques: - Model-directed Web Transaction - Merchant-Side Web Transaction - Context Browsing with Mobile - Context-directed Web Transaction • Evaluation: • Future Work:

3. Using PDA Browsing with Handheld User needs to do a lot of scrolling to get to the relevant content Relevant Content

4. Problems • Small Screens Offer Narrow Interaction Bandwidth. • Unable to convey the Richness of the Web content. • Involves a Lot of Horizontal and Vertical Scrolling. • Tedious to Get to the Pertinent Content in a Page. • This is worse when one is interested in Web transactions (e.g. buying books, paying utility bills).

5. Irrelevantcontent Relevant content Our Approach • Filter Away Irrelevant Content and Only Present • Relevant Content • First Present the Relevant Content.

6. Model-directed Web Transaction • Web Transaction Examples: - Buying a CD Player from Bestbuy - Paying Utility Bills Online • Web Transaction Characteristics: -A Sequence of Steps - Each Step is Based on User-Selected Operation • Two aspects of a Web transaction: - Semantic Concept - Process Model

7. Taxonomy Search Results Add to Cart Product Details Semantic Concepts

8. Process Model SEARCH FORM CONCEPT submit_searchform 1 item_select TAXONOMY CONCEPT

9. Process Model item_select select_item_category 1 submit_searchform

10. 2 Process Model SEARCH FORM CONCEPT submit_searchform item_select SEARCH RESULT CONCEPT

11. Process Model item_select item_select 1 submit_searchform add_to_cart select_item_category 2 submit_searchform

12. Process Model 1 - START STATE add_to_cart 6 - FINAL STATE show_item_detail add_to_cart check_out 3 4 item_select item_select view_shoppingcart item_select select_item_category submit_searchform Submit_searchform 1 6 check_out submit_searchform add_to_cart check_out select_item_category 2 5 submit_searchform view_shoppingcart, update_shoppingcart continue_shopping Model-driven transaction

13. Process Model 1 - START STATE add_to_cart 6 - FINAL STATE show_item_detail add_to_cart check_out 3 4 item_select item_select view_shoppingcart item_select select_item_category submit_searchform Submit_searchform 1 6 check_out submit_searchform add_to_cart check_out select_item_category 2 5 submit_searchform view_shoppingcart, update_shoppingcart continue_shopping Model-driven transaction

14. Evaluation Results Process Model • Built using Automata Learning Techniques • Training Data • Over 200 Transaction Sequences Collected from over 30 Sites • Recall / Precision • 90% / 96% for Books domain • 86% / 88% for Consumer Electronics domain • 84% / 92% for Office Supplies domain

15. Electronics Camera Search Result Taxonomy Search Form Software Search Phrase Image Sort Results By Insignia Electronics Select Box Select Box Browse Camera Entire Site Best Matches Image Electronics Software Brand Sony Go Button Image Browse Insignia Browse Image Image Case Logic Sony Browse Browse Image Case Logic Browse LOGICAL TREE CONCEPT TREE Concept Extraction

16. Evaluation Results Concept Extraction • Developed a Statistical Model for Each Concept using Machine Learning Techniques • Training Data • Used Labeled Concepts from Over 100 Pages Collected from Two Dozen Sites

17. Recall for Concept Extraction Evaluation Results

18. Guide-O-Mobile Guide-O Mobile Model-directed Web Transaction on Handheld: Guide-O-Mobile

19. Outline • Browsing with Handhelds: • Content Analysis Techniques: - Model-directed Web transaction - Merchant-Side Process Modeling - Context-Browsing with Mobile - Context-Directed Web Transaction • Evaluation: • Future Work:

20. Client-Side Process Modeling: Problems • Client-Side Process Modeling in Guide-O-Mobile. • Process Model is Stored in Client Side. • Separate Process Model Needed for Each Domain. • Performance Largely Depends on Concept Extraction.

21. Merchant-Side Process Modeling • Labeled Web Content with Semantic Annotations. • Content Providers will Label their Web Content. • XHTML will be Used to • Label Relevant Content in the Web Sites • Describe Process Models Specific to the Sites. • Mobile Users will Use the System to • Easily Identify Relevant Information. • Perform On-Line Transactions.

22. Prototype Implementation • XHTML tags: <log in>, <continue shopping>, <add to cart>, <edit cart>, <search form>, <search result>, <item>, <item taxonomy>, <item list>, <item detail>, <item description>, and <checkout>.

23. Outline • Browsing with Handhelds: • Content Analysis Techniques: - Model-directed Web Transaction - Merchant-side Web Transaction -Context-Browsing with Mobile - Context-Directed Web Transaction • Evaluation: • Future Work:

24. Context Browsing with Mobile • On Following a Link • Collect Context of the Link • Identify the Relevant Section on the Next Page • Using the Context • Present the Relevant Section. • Context Browsing • Reduces Information Overload • Makes Mobile Browsing Faster.

25. Context-directed Browsing

26. Context-directed Browsing

27. How Do We Find Relevant Content? • Finding What is Important on a Web Page: • Is Subjective on Any Distinct Page • Can be Inferred in a Sequence of Pages

28. Click on the “MP3 Players" Link Collect Context of the Link

29. Find Relevant Section Using Context Collect Context of the Link Click the Link – Collect Context

30. Find Relevant Section Using Context Click the Link – Collect Context

31. Context Browsing with Mobile: CMo Prototype

32. Product Search Using CMo

33. Outline • Browsing with Handhelds: • Content Analysis Techniques: - Model-directed Web transaction - Merchant-side Web transaction - Context-Browsing with Mobile -Context-directed Web Transaction • Evaluation: • Future Work:

34. Context-directed Web Transaction • No Process Model • Contextual Browsing with a Domain-Dependent Knowledge-Base • Relevant Segment Identification Using Contextual Browsing • Concept Segment Identification Using Knowledge-Base and Heuristics Algorithms

35. Context-directed Web Transaction: Prototype System • The Online Shopping Knowledge-Base Consists of the Following Few Concepts: SearchForm, AddToCart, Taxonomy, ShoppingCart, Checkout, etc. • Implementing the Prototype is a Work in Progress.

36. Evaluation: Guide-O-Mobile Experimental Set-Up • Guide-O-Mobile • 1.2 GHz desktop with 256 MB RAM • Client-Server Model • Client: 400 MHz iPaq with 64 MB RAM • Server: Core Guide-O System • Evaluation • Over two dozen CS graduate students • Over 30 web sites spanning Books, Consumer Electronics and Office Supplies domains

37. Evaluation: Guide-O Mobile Guide-O-Mobile: Overall Time Performance

38. Standard Deviation Evaluation: Guide-O Mobile Guide-O-Mobile Overall Time Performance– with standard deviation

39. Evaluation: Guide-O Mobile Guide-O-Mobile: Interaction Time

40. Evaluation: Guide-O Mobile Guide-O-Mobile Interaction Time Performance– with standard deviation Standard Deviation

41. Evaluation:CMo Experimental Set-Up • Client-Server Model • Client: IPAQ Pocket PC equipped with Microsoft Pocket PC operating system with wireless Internet connectivity. • Server: Core CMo System • Evaluation • 8 CS graduate studentscompleting 8 tasks (8 times each) on 8 Web sites from News and Shopping Domain.

42. Evaluation:CMoPerformance of Context Identification

43. Evaluation: CMoRelevant Information Identification

44. Browsing Efficiency with CMo

45. Conclusion and Future Work • Port all the Server Steps to the Handheld. • Extend the Mozilla's Minimo Mobile Browser with CMo Functionalities. • Mining Transactional Models from Contextual Information.

46. Questions?