recommendations at senscritique. mixing social and machine learning

Recommendations at SensCritique Mixing social and machine learning

By Xavier RAMPINO, Mars 2016

What is SensCritique ?

Entertainment database


Movies, TV Shows,

Video Games, Books,

Comics, Music



Movies, TV Shows,

Video Games, Books,

Comics, Music

Entertainment database Virtual library


Movies, TV Shows,

Video Games, Books,

Comics, Music

Entertainment database Virtual library

Rate, List,

Review, add to a wish list


Movies, TV Shows,

Video Games, Books,

Comics, Music

Entertainment database Virtual library Social network

Rate, List,



Movies, TV Shows,

Video Games, Books,

Comics, Music

Entertainment database Virtual library Social network

Rate, List,


Users can : Follow each other,

like activities, recommend products


Database + Library + Network




=



Discovery Engine


Discovery tool


Discovery Engine


Discovery tool

Discovery tool


Discovery Engine


Discovery tool

Discovery tool

Discovery tool


Discovery Engine


14M

600K

55M

1,5M

products

registered users

ratings

lists

How we make it work

How we make it work

• Multimedia Database


How we make it work


• Extensive Cinema, TV, and VOD showtimes


How we make it work


• Extensive Cinema, TV, and VOD showtimes

• Graph Database


How we make it work

Key factors for discovery :

• Serendipity • Familiar cues (actors, franchises) • Familiar media (TV, cinema)


How we make it work

Virtual Library

We have very enthusiastic members

How we make it work

Virtual Library

Our past recommendation engine stack

How we make it work

Virtual Library


Ratings

Wish

CSV dump LensKit

MySql

How we make it work

Virtual Library


Very extensive operation, once a day

Ratings

Wish

CSV dump LensKit

MySql

How we make it work

Virtual Library

Our current recommendation engine stack

How we make it work

Virtual Library


Nginx Logs

Web hooks

prediction.io event server Application

How we make it work

Virtual Library


Nginx Logs

Web hooks

prediction.io event server Application

• Live Update • Take product views into account • Live boosting during querying (via Elasticsearch)

How we make it work

Social Network

We use social recommendations at two levels

How we make it work

Social Network


User-User recommendation Live Feed

Direct

How we make it work

Social Network


User-User recommendation Live Feed

Direct

News filteringTops

Aggregated

How we make it work

Once one has chosen people to follow, he gets live update on their entertainment activity

Social NetworkDirect

How we make it work

He may also be given direct recommendation


How we make it work

To allow one member to find similar members, we made a user-user recommendation engine using Mahout :


Online recommendation system, webservice served over Glassfish Event updates are sent on RabbitMQ, then dispatched to a Java Consumer

How we make it work

One member can notice than a subgroup of member he follows did an activity on a specific product

Social NetworkAggregated

How we make it work

Community choices, given an editorial theme Their own activity (we’ll show them only polls for which

they already rated some of the result products)


We have also polls, in which all members can participate. Member then get recommendations fuelled by two forces :

How we make it work


Conclusion

From its origins, SensCritique is headed towards product discovery, as a whole though 3 ways :

• Database : We tend to have a comprehensive and ambitious graph-oriented database

• Library : We are a tool for the user to keep a memory of his entertainment history

• Social : We foster social activity between our members, and we are proud of our community

We strongly believe that this is thanks to these three factors that we can offer both a tailored and a global recommendation offer to our audience.

Questions ?

Question No question

recommendations at senscritique. mixing social and machine learning

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