1 Whats Up: P2P news recommender Anne-Marie Kermarrec Joint work with Antoine Boutet, Davide Frey (INRIA) and Rachid Guerraoui (EPFL) Gossple workshop

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What’s Up:P2P news recommender

Anne-Marie Kermarrec

Joint work with Antoine Boutet,Davide Frey (INRIA) and Rachid Guerraoui (EPFL)

Gossple workshop 2010

Gossple workshop 2010 2

The social Web

Web content is generated by you, me, your friends and millions of others

The Web has turned social

Content comes from everywhere


Is it equally relevant?






What’s wrong with news feed?

Amazon recommends me a fryer

Some of my Facebook write in Italian

LeMonde.fr wants to inform me on the Champion’s ligue


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 pub/sub do not filter enough

Granularity of a user seems too coarse


Cascading over explicit links


Fine grain tuning calls fordecentralisation


What’s up

• Decentralised information dissemination channel

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

• Exploit implicit social links


An implicit pub/sub system


What’s up in a nutshell


What’s up challenges


•Who are my social acquaintances

•How to discover them?

•How to disseminate news ?

Similarity metric

Through gossip

Biased epidemic protocol

What’s up: Gossple net







Similarity metric

An implicit social network


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

• Multi-interest similarity







Through gossip

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The Gossple network


Copyright: E. Rivière

Gossip similarity protocol.

Gossip-based peer sampling service


Building the social network

• Two gossip protocols• Similarity-based Peer Sampling• Random Peer Sampling

• When p encounters q• Evaluate potential new view, based on set

similarity metric• Use of Bloom filters to limit the communication

overhead

RPS

SPS

RPS

SPS

What’s up in a nutshell






•How to disseminate news ? Biased epidemic protocol

Dissemination


Heterogeneous

Homogeneous

HeterogeneousHomogeneous

Involvement (fanout)

Expectations

EpidemicDissemination

F=log(N)

HeterogeneousGossip

F≈ log(N) on average

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BEEP: orientation and amplification

Orientation: to whom?


Forwardto

friends

Forwardto

random

Amplification: to how many?

Increase fanout

Decreasefanout

Beep: I like it


I like it!

Beep: I don’t


I dislike it!

Tuning BEEP

• Orientation• The news carries the list of visited users• A profile: sum of interests of users who liked it

• Amplification✔ F≈ log(N) friends✔ Amplification depends on the similarity between the

news and the user✖ F≈ 1 or 2 random


Evaluation

• User Metrics• Spam• Recall• Precision

• System metric• Number of messages• Redundancy (useless messages)

• Traces• Synthetic clustered traces• Real dataset: 700 Digg users/2000 news/1 week


Preliminary results


Algorithm Precision Recall Spam

Perfect 1 1 0

Gossip fanout=log(n)=7

0.28 0.94 0.74

Cascading through explicit friends from Digg

0.39 0.71 0.71

WhatsUp fanout=11/1 ; ttl=12

0. 52 0.6

WhatsUp without no social users

To take away

• Automatic light news recommender

• Analysis through mean field theory

• Experimental evaluation

Next: diversity of sources, trust, privacy


Thank you

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www.gossple.fr



Documents

1 Whats Up: P2P news recommender Anne-Marie Kermarrec Joint work with Antoine Boutet, Davide Frey (INRIA) and Rachid Guerraoui (EPFL) Gossple workshop