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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
3 From a toolkit of recommendation algorithms into a real business 13.09.2012.
4. What we do?
users
content of service
provider
recommender
4 From a toolkit of recommendation algorithms into a real business 13.09.2012.
5. Explicit vs implicit feedback
No ratings but interactions
sparse vs. dense matrix
requires different learning
5 From a toolkit of recommendation algorithms into a real business 13.09.2012.
6. Increase revenue: A/B tests
against the original solution
internally
6 From a toolkit of recommendation algorithms into a real business 13.09.2012.
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
7 From a toolkit of recommendation algorithms into a real business 13.09.2012.
8. Time requirements
⢠Response time: few ms (max 200)
⢠Training time: maximum few hours
⢠regular retraining
⢠incremental training
⢠Newsletters:
⢠nightly batch run
8 From a toolkit of recommendation algorithms into a real business 13.09.2012.
9. Productization
IMPRESS RECO ADâ˘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
9 From a toolkit of recommendation algorithms into a real business 13.09.2012.
10. The 5% question â Importance of UI
Francisco Martin (Strands): âthe algorithm is only 5% in the success of
the recommender systemâ
⢠placement
ď§ below or above the fold
ď§ scrolling
ď§ easy to recognize
ď§ floating in
⢠title
ď§ not misleading
ď§ explanation like
⢠widget
ď§ carrousel
ď§ static
10 From a toolkit of recommendation algorithms into a real business 13.09.2012.
11. Recommendation scenario
Item2Item
recommendation
logic: the adâs
profile will be
matched to the
profile model of
available ads
11 From a toolkit of recommendation algorithms into a real business 13.09.2012.
12. Marketing channels
Changing the order of two boxes: 25% CTR increase
12 From a toolkit of recommendation algorithms into a real business 13.09.2012.
13. Cannibalization
⢠Goal: increase user engagement
⢠Measurements
⢠average visit length
⢠average page views
⢠Effect of accurate recommendations:
⢠use of listing page â
⢠use of item page â
⢠Overall page view: remains the same
⢠Secondary measurements
⢠Contacting
⢠CTR increase
13 From a toolkit of recommendation algorithms into a real business 13.09.2012.
14. Evolution: increased user engagement
⢠not a cold start problem
⢠parameter optimization and user engagement
14 From a toolkit of recommendation algorithms into a real business 13.09.2012.
15. KPIs â may change during testing
15 From a toolkit of recommendation algorithms into a real business 13.09.2012.
16. Complete personalization: coupon-world
⢠Newsletter (daily +
occassionally)
⢠Ranking all offers on the website
⢠top1 item
⢠category preferences
⢠user metadata (gender, age, âŚ)
⢠user category preferences
(seldom given)
⢠item metadata
⢠context
⢠customer vs. vendor
16 From a toolkit of recommendation algorithms into a real business 13.09.2012.
17. Business rules â driving/overriding ranking
17 From a toolkit of recommendation algorithms into a real business 13.09.2012.
18. Contexts
18 From a toolkit of recommendation algorithms into a real business 13.09.2012.
19. Context at TV program recommendation
⢠TV (EPG program & video-on-demand)
ď§ explicit and implicit identification of the user in the household
ď§ time-dependent recommendation
19 From a toolkit of recommendation algorithms into a real business 13.09.2012.
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%
20 From a toolkit of recommendation algorithms into a real business 13.09.2012.
21. Anecdotes
⢠Item2item recommendations â bookstore
⢠Placebo effect
⢠buyer vs. seller
21 From a toolkit of recommendation algorithms into a real business 13.09.2012.
22. Conclusion
⢠Offline and online testing
⢠From simple to sophisticated
⢠Many more potential fields of application
22 From a toolkit of recommendation algorithms into a real business 13.09.2012.