4. How to feed a recommender?
●
○ recommendations & advertising network
○ ~5k recs/ second
● integrated on publishers
○ welt.de, abendblatt, etc
5. How to feed a recommender?
● to show recommendations we integrated javascript
● we have URL + HTTP Headers
○ user agent
○ IP address -> geolocation
src http://en.wikipedia.org/wiki/Pac-Man
5
10. ● Data Types: String, Lists, Set, ..
● Hash
○ map between string fields and string values, very
fast
○ HINCR complexity O(1)
● Sorted Set
○ ZINCR complexity: O(log(N)) where N is the number
of elements in the sorted set.
○ Allows to limit number of result:
ZREVRANGEBYSCORE
○ UNION + INTERSECT
How to pass data quickly?
p:welt.de
berlin_wins 689 +1
summer_is_coming 420
plista_company 135
10
12. How to build a recommendation?
Behavioral
based on interaction
between user and
article
○ Most Popular
○ Collaborative Filtering
○ Item to Item
Content
based on the articles
○ Content Similarity
○ Latest Item
Classification
● different recommender families
13. How to build a recommendation?
welt.de/football/berlin_wins.html
● ZINCR "p:welt.de" berlin_wins
● ZREVRANGEBYSCORE
p:welt.de
berlin_wins 689 +1
summer_is_coming 420
plista_company 135
Live Read
+ Live Write
= Real Time Recommendations
16. Most popular with timeseries
:1360007000
-1h -2h -3h -4h -5h -6h -7h -8h
:1360007000
:1360007000
4
2
1
17. Most popular to any context
● it's not only publisher, we use ~50 context
attributes
context attributes:
● publisher
● weekday
● geolocation
● demographics
● ...
publisher = welt.de
berlin_wins 689 +1
summer_is_coming 420
plista_company 135
weekday = sunday
berlin_wins 400 +1
dortmund_wins 200
... 100
geolocation = dortmund
dortmund_wins 200
berlin_wins 10 +1
... 5
18. Most popular to any context
ZUNION ... WEIGHTS
p:welt.de:1360007 4
p:welt.de:1360006 2
p:welt.de:1360005 1
w:sunday:1360007 4
w:sunday:1360006 2
w:sunday:1360005 1
g:dortmund:1360007 4
g:dortmund:1360006 2
g:dortmund:1360005 1
● how it looks like in Redis
publisher = welt.de
berlin_wins 689 +1
summer_is_coming 420
plista_company 135
weekday = sunday
berlin_wins 400
dortmund_wins 200
... 100
geolocation = dortmund
dortmund_wins 200
berlin_wins 10
... 5
19. Even more Matrix Operations ;)
● Similarity Matrix
● Human Control Matrix
● Meta-learning Matrix
○ cooperation with
○ aided from
∏
Σ
20. More recommenders possible
this was only about most popular
● other algorithms using redis
○ incremental collaborative filtering
○ article to article paths (~graph)
○ .. using external data sources
srchttp://en.wikipedia.org/wiki/Flash_(comics)
20
21. What else in Redis?
● message bus
● many recommenders
● live statistics
● caching
"One technology to rule them all"
