1 / 12

Job Clouds

Job Clouds. Presented by: Laura Bright and Brian Lewis May 1st, 2006 Semantic Web / INF 385T. Presentation Overview. Introduction to Topic Project Goals Technologies Used Usage Scenario Job Clouds Demo Limitations Future Development Conclusions Discussion.

colman
Download Presentation

Job Clouds

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Job Clouds Presented by: Laura Bright and Brian Lewis May 1st, 2006 Semantic Web / INF 385T

  2. Presentation Overview • Introduction to Topic • Project Goals • Technologies Used • Usage Scenario • Job Clouds Demo • Limitations • Future Development • Conclusions • Discussion

  3. Introduction to Topic • Job search portal with a visual component • A way to search for job listings and see a visualization of popular search terms over time within the site community via a tag cloud • Main Benefits • Shows search terms in the tag cloud as they have appeared over time within the community • Suggests other search terms that might be relevant to a search you are performing

  4. Project Goals • To develop an online application that: • Provides a community based job search portal for current job seekers • Has relevant and timely job listings per keyword and location specifications • Provides a weighted list of searched terms based on what the users (the community) have inquired about • Has a tag cloud that creates a visualization of searched terms weighted and styled according to frequency of use and timeframe

  5. Technologies Used

  6. Tag Cloud Specifications • Weighting terms according to: • Popularity • Freshness • Keyword terms are clickable

  7. Job Clouds Demo

  8. Limitations • Poor Data Quality • Web Services not Mature Enough • Listerizer (Low quality) • Zoom Clouds (Low flexibility) • Limited Search Fidelity

  9. Future Development • Reveal relationships in tag visualization • Create more search parameters • Salary • Company Name • Company Size • Rank search terms according to how well they match given keywords

  10. Conclusions • Web services must be highly customized in order to provide specific value • Mash-ups are still programming intensive and therefore have high entry costs • Job clouds service should provide more data entry points to facilitate more robust search capabilities • Possibly deploy service for iSchool grad students on UT ASIST website

  11. Discussion Questions or Comments? Thank you for your time!

More Related