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Newsletters An automatic news recommender system

Newsletters An automatic news recommender system. Manish Agrawal March 12, 2008. Motivation. Many data sources available online Hard to keep track of all sources Many sources might have similar content Need to organize information by interest. Crawler. Meta Data (RDBMS).

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Newsletters An automatic news recommender system

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  1. NewslettersAn automatic news recommender system Manish Agrawal March 12, 2008

  2. Motivation • Many data sources available online • Hard to keep track of all sources • Many sources might have similar content • Need to organize information by interest

  3. Crawler Meta Data (RDBMS) Register Community Clustering Indexer Query Index Date-wise Index Re-ranking based on feedback (if available) Newsletter DB Architecture Manish Agrawal, V.G.Vinod Vydiswaran and Kamal K. Gupta: “Automated generation of interest based newsletters”

  4. Clustering and re-ranking Crawling and Indexing Web Server FacebookAppl (GUI) Newsletter DB Newsletter Every day… Manish Agrawal, V.G.Vinod Vydiswaran and Kamal K. Gupta: “ Automated generation of interest based newsletters”

  5. Why Facebook? • Provides a great platform with in built social networks • Possible to make applications that deeply integrate into a user's Facebook experience. • FBML (Facebook Markup language) • FBJS (Facebook Javascript) • FQL (Facebook Query Language) • Facebook API

  6. Anatomy of a Facebook App (Integration Points) • Product directory • Left Navigation • Facebook Canvas Pages • Profile Box (Content cached on Facebook server) • Privacy settings • Mini-Feed • News-Feed • Alerts (E.g. Notification) • Requests (E.g. an invitation)

  7. Screen Snapshots (Canvas Page)

  8. Screen Snapshots (Profile Box)

  9. Screen Snapshots (Mini Feed)

  10. Future scope • More Personalization: Presently the newsletter is community specific. It can be personalized for individual Facebook user by exploiting user profiles and their friends’ profiles. • User feedback data (click-throughs, ratings, recommendations) can be used to improve the ranking algorithm and better model the community and the user

  11. Thank you!

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