1. A Novel Visualization Model for Web Search Results Nguyen T, and Zhang J. 2006. IEEE Transactions on Visualization and Computer Graphics PAWS Meeting Presented by Jae-wook Ahn February 9, 2007

2. Motivations Search result presentation Search result presentation Linear ranked list vs. visualization Linear ranked list vs. visualization Interactivity Interactivity Exploration Exploration Discover and analyze information by users Discover and analyze information by users Simple term based visualization models Simple term based visualization models Multiple meanings Multiple meanings Order of keywords Order of keywords Missing context information Missing context information

3. Motivations (cont’d) What is missing with current approaches? What is missing with current approaches? Semantic views/semantic relations Semantic views/semantic relations Degree of relevance in terms of subjects of interest Degree of relevance in terms of subjects of interest Visualization model adapting to users’ subjects of interest and contextual information Visualization model adapting to users’ subjects of interest and contextual information

4. Proposed approach Metaphor – solar system Metaphor – solar system Query – Sun Query – Sun Documents – planets Documents – planets Dimensions and attributes Dimensions and attributes Semantic strength (= relevance) Semantic strength (= relevance) Gravity = (distance) Gravity = (distance) Rotation speed Rotation speed Color Color What is unique? What is unique? Use movement, speed, and distance to visualize the degree of relevance among a query and Web search results with respect to users’ subjects of interest and contextual information Use movement, speed, and distance to visualize the degree of relevance among a query and Web search results with respect to users’ subjects of interest and contextual information

5. Architecture WebSearchViz – Java based meta search (Google) visualization WebSearchViz – Java based meta search (Google) visualization Vector space model – document-term matrix Vector space model – document-term matrix Term weighting Term weighting Similarity measure Similarity measure

6. Subject of interest Subject = List of keywords Subject = List of keywords Users can manually add/remove/edit subject of interests Users can manually add/remove/edit subject of interests Weights are adjustable Weights are adjustable

7. Visualization space Location of documents Location of documents Similarity to the query Similarity to the query More similar, closer More similar, closer Similarity to the subjects Similarity to the subjects More similar, closer More similar, closer Rotation speed of the documents Rotation speed of the documents Similarity to the subjects Similarity to the subjects More similar, more identical to the speed of the subjects More similar, more identical to the speed of the subjects Computed by the angles (  i ) Computed by the angles (  i ) Rotation Rotation Automatic or manual Automatic or manual Query Document Subject of interest

8. WebSearchViz (1) Google’s search results (2) Users’ subjects (3) Visualization (4) Rotation control

9. Additional features Manual rotation Manual rotation Users can view the impact of a moving subject Users can view the impact of a moving subject Colors Colors Users can mark a document and can keep track it through sessions Users can mark a document and can keep track it through sessions Center switching Center switching Any page can be made the center Any page can be made the center Filtering Filtering Users select a threshold by creating a filter circle Users select a threshold by creating a filter circle Filter out low similarity documents Filter out low similarity documents Grouping Grouping Visualize only a group of documents Visualize only a group of documents

10. Experiments Time efficiency Time efficiency 0.7 sec (parsing & indexing) 0.7 sec (parsing & indexing) 0.5 sec (document-term matrix, 10,000 page, 54,000 keywords) 0.5 sec (document-term matrix, 10,000 page, 54,000 keywords) Initial Visualization rendering – 1.2 sec Initial Visualization rendering – 1.2 sec Usability study Usability study 20 undergraduates (natural & social science) 20 undergraduates (natural & social science) Questionnaire & accuracy evaluation Questionnaire & accuracy evaluation 89% recall, 92% precision 89% recall, 92% precision More successful with WebSearchViz More successful with WebSearchViz Subjects liked more manual rotation Subjects liked more manual rotation Subjects liked grouping, filtering, focus page shifting Subjects liked grouping, filtering, focus page shifting Short learning curve – enhancement (WebSearchViz) to an existing search service (Google) Short learning curve – enhancement (WebSearchViz) to an existing search service (Google)

11. Conclusions Web search visualization Web search visualization Considers context – subjects of interests Considers context – subjects of interests Subject editor Subject editor New dimension – rotation speed New dimension – rotation speed Not just decorative Not just decorative