Nocturnal: Automated Messenger-based Information Sharing and Collaboration Coby Fernandess (Intern) the Hebrew University Dahlia Malkhi, Udi Wieder, Lidong.

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Presentation transcript:

Nocturnal: Automated Messenger-based Information Sharing and Collaboration Coby Fernandess (Intern) the Hebrew University Dahlia Malkhi, Udi Wieder, Lidong Zhou MSR, Silicon Valley

2 Nocturnal A collaborative personalized search tool Messenger-based sharing of URL-ratings Local Data Base 1. Exchange URL Ratings 2. Store in local DB 3. Web search results compared with local DB 4. Personalized, collaborative search 5. Feed back satisfaction rating to local DB

3 Nocturnal A collaborative personalized search tool Messenger-based sharing of URL-rating Local Data Base 1. Exchange URL Ratings 2. Store in local DB 3. Web search results compared with local DB 4. Personalized, collaborative search 5. Feed back satisfaction rating to local DB

4 Nocturnal Vision Messenger as a Social Infrastructure for Communities. Decentralized social information sharing. –Data propagates to 1-hop: a user s contacts 2-hop: contacts contacts 3-hop: contacts contacts contacts and so on Collaborative and personalized web search. –Each user stores information locally in a database My Contacts: small community My contacts contacts

5 Messenger as a Social Infrastructure for Communities. Social communities are a powerful tool but social connections are hard to elicit from users. MSN messenger is an existing large social network –Solves the bootstrapping problem. –Yields effective communities (see poster).

6 Decentralized social information sharing. No central repository – P2P paradigm –Personalization: Each user has distinct data –Privacy: User information propagates only to trusted peers –Trust: Information is pulled only from trusted peers

7 Collaborative and personalized web search. Things you can do with Nocturnal: –Share URL ratings among Messenger peers –Collaborative web spam filtering –Exchange reviews and recommendations A browser enhanced with our toolbar uses the local database to filter search results and to give peer-recommended URLs higher importance.

8 Research Challenges Properties of Messenger-Based Communities and Implications on Information Sharing and Dissemination. Trust and Reputation on a Social Network. Collaborative and Personalized Search: information gathering, filtering, aggregation and ranking.

9