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Those Internet users who wish you well

Anne-Marie Kermarrec

INRIA

18 mai 2011

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The new face of the Web

user-generated

social

Search is over…

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The content comes from dedicated channels

Is it equally relevant?

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Is it equally relevant?

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Is it equally relevant?

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Cascading over explicit links

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What’s wrong with news feed?

Amazon recommends me a fryer

Some of my Facebook friends write in Italian

LeMonde.fr informs me on the Champion’s ligue

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Why is it so difficult?

• Even a space restricted to users’ explicit subscriptions is too large a database

• Dynamic

• Recommendations not always user-centric

• Explicit links not always that relevant

• Classical notification systems do not filter enough

Granularity of a user seems too coarse

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Fine grain tuning requires personalization

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Where does the shoe pinch ?

Scalability and Privacy

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Personalization calls for decentralization

Our approach: WhatsUp

• Decentralized information dissemination channel

• Simple interface: I like it or I don’t

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WhatsUp in a nutshell

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WhatsUp’s challenges

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• Who are my social acquaintances

• How to discover them?

• How to disseminate news ?

Similarity metric

Through gossip

Biased epidemic

protocol

The implicit social network

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WhatsUp’s challenges

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• Who are my social acquaintances

• How to discover them?

• How to disseminate news ?

Similarity metric

An implicit social network

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Which nodes should be considered as social acquaintances?

Model

• U(sers) × I(tems) (news)

• Profile(u) = vector of liked news

• Minimal information

Similarity metrics

• Overlap

• Cosine similarity

• Anti-spam similarity

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Item cosine similarity

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Normalized overlap

Profile(u)= Vector of news Items()

What’s up challenges

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• Who are my social acquaintances

• How to discover them?

• How to disseminate news ?

Through gossip

Each node maintains a set of neighbors (k entries)

P Q

Data exchange

Data processing

Peer selection

Parameter Space

Gossip-based computing

Random Peer sampling [ACM TOCS 2007] achieves random

topologies

Sampling the network withgossip

Similarity computation

The Gossple social network

Gossip-based peer sampling service

Copyright: E. Rivière

Gossip similarity protocol.

WhatsUp in a nutshell

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WhatsUp’s challenges

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• Who are my social acquaintances

• How to discover them?

• How to disseminate news ? Biased epidemic

protocol

Epidemic dissemination

• The log(n) magic

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Early

adopters

Innovators

Early

majority

Late

majority

Laggards

Broadcast

Contagion

Dissemination

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Heterogeneous

Homogeneous

HeterogeneousHomogeneous

Involvement (fanout)

Expecta

tions

Epidemic

Dissemination

F=log(N)

Heterogeneous

Gossip

F≈ log(N) on

average

BEEP: orientation and amplification

Orientation: to whom?

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Forward

to

friends

Forward to

Random

users

Amplification: to how many?

Increase

fanout

Decrease

fanout

Tuning BEEP

• Orientation

• Like/dislike-dependent

• News-based orientation

• Amplification

�I like it: large fanout

• F≈ log(N) friends in the social network

�I don’t like it: small fanout

� F≈ 1 or 2 according to the news item profile

• Natural fanout adaptation

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Beep: I like it

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Beep: I don’t

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Evaluation

• User Metrics

• Recall

• Precision

• System metric

• Number of messages

• Redundancy (useless messages)

• Traces

• Real trace from a 480 user survey on 1000 news items

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WhatsUp in action

Precision Recall Redundancy Messages

Gossip 0.34 0.99 0.85 2.3 M

Cosine-CF 0.64 0.12 0.27 30k

Whatsup 0.53 0.78 0.28 280k

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WhatsUp in action

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Fanout

WhatsUp

The more sociable, the better

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F1

-S

core

Sociability

To take away

Personalization is needed

Decentralization is healthy

Gossip is the way to go

Thank you

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www.gossple.fr

18 mai 2011

Joint work with A. Boutet (INRIA), D. Frey (INRIA), A. Jégou (INRIA) and

R. Guerraoui (EPFL)