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Content Analysis Techniques to Ease Browsing with Handhelds Jalal Mahmud Yevgen Borodin I.V. Ramakrishnan Department of Computer Science State University.

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Presentation on theme: "Content Analysis Techniques to Ease Browsing with Handhelds Jalal Mahmud Yevgen Borodin I.V. Ramakrishnan Department of Computer Science State University."— Presentation transcript:

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 Browsing with Handheld User needs to do a lot of scrolling to get to the relevant content Using PDA 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 Our Approach Relevant content Irrelevant content 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 Semantic Concepts Search Results Taxonomy Add to Cart Product Details

8 item_select submit_searchform Process Model TAXONOMY CONCEPT SEARCH FORM CONCEPT 1

9 select_item_category item_select submit_searchform Process Model 1

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

11 item_select select_item_category item_select submit_searchform 2 add_to_cart submit_searchform Process Model 1

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

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

14 Evaluation Results 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 Process Model

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

16 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 Evaluation Results Concept Extraction

17 Evaluation Results Recall for Concept Extraction

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

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

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 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 Context-directed Web Transaction

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 Evaluation: Guide-O Mobile Guide-O-Mobile Overall Time Performance– with standard deviation 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 students completing 8 tasks (8 times each) on 8 Web sites from News and Shopping Domain.

42 Evaluation:CMo Performance of Context Identification

43 Evaluation: CMo Relevant 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?


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