From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience (talk given at Recsys 2012)
1. From a toolkit of
recommendation algorithms
into a real business:
the Gravity R&D experience
13.09.2012.
2. The kick-start
2 From a toolkit of recommendation algorithms into a real business 13.09.2012.
3. Facing with real needs
What we had What clients wanted
• rating prediction algorithms • recommendations that
• coded in various languages bring revenue
• blending mechanism • robustness
• accuracy oriented • low response time
• easy integration
• reporting
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4. What we do?
users
content of service
provider
recommender
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5. Explicit vs implicit feedback
No ratings but interactions
sparse vs. dense matrix
requires different learning
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6. Increase revenue: A/B tests
against the original solution
internally
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7. Robustness
Management LAN
SNMP
Nagios Monitoring HP OpenView
Aggregator
HTTP HTTP
Platform OSS/BSS / SQL / SQL
IMPRESS IMPRESS
SOAP Application Server #1 Application Server #2
IMPRESS Frontend
web server #1
Backend LAN Reco LAN HTTP Load Balancer HTTP(S)
Firewall SQL SQL
CSV over FTP
TV Service LAN
IMPRESS Frontend
web server #2
Database #1 Database #2
Reporting Subsystem
End users
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8. Time requirements
• Response time: few ms (max 200)
• Training time: maximum few hours
• regular retraining
• incremental training
• Newsletters:
• nightly batch run
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9. Productization
IMPRESS RECO AD•APT
for for for
IPTV, CATV and satellite e-commerce ad networks and ad
server providers
Recommends Recommends Recommends Personally
Personally Relevant Relevant
Personally Relevant
products & services ads
Linear TV, VOD,
catch-up TV and more
Gravity personalization platform
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10. The 5% question – Importance of UI
Francisco Martin (Strands): „the algorithm is only 5% in the success of
the recommender system”
• placement
below or above the fold
scrolling
easy to recognize
floating in
• title
not misleading
explanation like
• widget
carrousel
static
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11. Recommendation scenario
Item2Item
recommendation
logic: the ad’s
profile will be
matched to the
profile model of
available ads
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12. Marketing channels
Changing the order of two boxes: 25% CTR increase
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13. Cannibalization
• Goal: increase user engagement
• Measurements
• average visit length
• average page views
• Effect of accurate recommendations:
• use of listing page ↓
• use of item page ↑
• Overall page view: remains the same
• Secondary measurements
• Contacting
• CTR increase
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14. Evolution: increased user engagement
• not a cold start problem
• parameter optimization and user engagement
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15. KPIs – may change during testing
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16. Complete personalization: coupon-world
• Newsletter (daily +
occassionally)
• Ranking all offers on the website
• top1 item
• category preferences
• user metadata (gender, age, …)
• user category preferences
(seldom given)
• item metadata
• context
• customer vs. vendor
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17. Business rules – driving/overriding ranking
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18. Contexts
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19. Context at TV program recommendation
• TV (EPG program & video-on-demand)
explicit and implicit identification of the user in the household
time-dependent recommendation
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20. (offline)
Some results (online)
Improvement using season
iTALS iTALSx
Dataset Recall@20 MAP@20 Recall@20 MAP@20
Grocery 64,31% 137,96% 89,99% 199,82%
TV1 14,77% 43,80% 28,66% 85,33%
TV2 -7,94% 10,69% 7,77% 14,15%
LastFM 96,10% 116,54% 40,98% 254,62%
Improvement using Seq
iTALS iTALSx
Dataset Recall@20 MAP@20 Recall@20 MAP@20
Grocery 84,48% 104,13% 108,83% 122,24%
TV1 36,15% 55,07% 26,14% 29,93%
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21. Anecdotes
• Item2item recommendations – bookstore
• Placebo effect
• buyer vs. seller
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22. Conclusion
• Offline and online testing
• From simple to sophisticated
• Many more potential fields of application
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