SlideShare a Scribd company logo
A recommendation engine
for your applications
Michele Orselli | ideato
AMSTERDAM 16 - 17 MAY 2017
Search Engines
Recommender systems
Non personalised
Hotel A Hotel B Hotel C
John 3 5
Jane 3
Fred 1 0
Tom 4
AVG 3.5 3 0
Content based
User rate items
We build a model of user preference
Look for similar items based on the model
Action 0.7
Sci Fi 3.2
Vin Diesel 1.2
… …
Title Star Trek
Genre Sci Fi
Actors
William Shatner
LeonardNimoy
User Model
Title XXX
Genre Action
Actors
Vin Diesel
Asia Argento
Title Kick Ass
Genre Action
Actors
Nicholas Cage
Mark Strong
Problems/Limitations
Need to know items content
User cold start: time to learn important features
for the user
What if user interest change?
Lack of serendipity: accidentally discover
something you like
Collaborative
filtering
User-User
Select people of my neighbourhood with
similar taste. If other people share my taste I
want their opinion combined
Item-Item
Find an items where I have expressed an
opinion and look how other people felt about
it
Problems/Limitations
Sparsity
When recommending from a large item set,
users will have rated only some of the items
User Cold start
Not enough known about new user to decide
who is similar
Item cold start
Cannot predict ratings for new item till some
similar users have rated it [No problem for
content-based]
Scalability
With millions of ratings, computations
become slow
An example
Item1 Item2 Item3 Item4 Item5
Joe 8 1 ? 2 7
Tom 2 ? 5 7 5
Alice 5 4 7 4 7
Bob 7 1 7 3 8
How similar are Joe and Tom?
How similar are Joe and Bob?
Only consider items both users have rated
For each item
- Compute difference in the users’ ratings
- Take the average of this difference over the items
Item1 Item2 Item3 Item4 Item5
Joe 8 1 ? 2 7
Tom 2 ? 5 7 5
Alice 5 4 7 4 7
Bob 7 1 7 3 8
Sim(Joe, Tom) = (|8-2| + |2-7| + |7-5|)/3 = 13/3 = 4.3
Sim(Joe, Alice) = (|8-5| + |1-4| + |2-4| + |7-7|)/4 = 2
Sim(Joe, Bob) = (|8-7| + |1-1| + |2-3| + |7-8|)/4 = 0.75
Item1 Item2 Item3 Item4 Item5
Joe 8 1 ? 2 7
Tom 2 ? 5 7 5
Alice 5 4 7 4 7
Bob 7 1 7 3 8
Similarity
Bob 1.57
Alice 0.33
Tom 0.18
D = 1 / 1 + d
Recommend what similar user have rated highly
To calculate rating of an item to recommend, give
weight to each user’s recommendations based on how
similar they are to you.
Rating(Joe, Item3) = (1.57 * 7 + 0.33 * 7 + 0.18 * 5) / 3
10.99 + 2.31 + 0.9 / 3 = 4.3
Similarity
Bob 1.57
Alice 0.33
Tom 0.18
use entire matrix or
use a K-nn algorithm: people who historically
have the same tastes as me
aggregate using weighted sum
weights depends on similarity
Our domain
Domain: online book shop, both paper and
digital
Recommend titles, old and new
- Who bought this also bought
- You might like
PredictionIO
Installation
http://actionml.com/docs/pio_by_actionml
Pre-baked Amazon AMIs
The event server
Pattern: user -- action -- item
User 1 purchased product X
User 2 viewed product Y
User 1 added product Z in the cart
$ pio app new MyApp1
[INFO] [App$] Initialized Event Store for this app ID: 1.

[INFO] [App$] Created new app:

[INFO] [App$] Name: MyApp1

[INFO] [App$] ID: 1

[INFO] [App$] Access Key:
3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F
$ pio eventserver
Server runs on port 7070 by default
$ curl -i -X GET http://localhost:7070
{“status":"alive"}
$ curl -i -X GET “http://localhost:
7070/events.json?
accessKey=$ACCESS_KEY"
Events modeling
what can/should we model?
rate, like, buy, view, depending on the
algorithm
setUser($uid, array $properties=array(), $eventTime=null)
unsetUser($uid, array $properties, $eventTime=null)
deleteUser($uid, $eventTime=null)
setItem($iid, array $properties=array(), $eventTime=null)
unsetItem($iid, array $properties, $eventTime=null)
deleteItem($iid, $eventTime=null)
recordUserActionOnItem($event, $uid, $iid, array
$properties=array(), $eventTime=null)
createEvent(array $data)
getEvent($eventId)
Engines
$ pio template get apache/incubator-
predictionio-template-recommender
MyRecommendation
$ cd MyRecommendation
engine.json
"datasource": {

"params" : {

"appName": “MyApp1”,

"eventNames": [“buy”, “view”] 

}

},
$ pio build —verbose
$ pio train
$ pio deploy
Getting
recommendations
Implementation
2 kind of suggestions
- who bought this also bought
(recommendation)
- you may like (similarities)
View
Like (add to basket, add to wishlist)
Conversion (buy)
Recorded in batch
4 engines
2 for books, 2 for ebooks
(not needed now)
Retrained every night with new data
recordLike($user, array $item)
recordConversion($user, array $item)
recordView($user, array $item)
createUser($uid)
getRecommendation($uid, $itype, $n=self::N_SUGGESTION)
getSimilarity($iid, $itype, $n=self::N_SUGGESTION)
Cold start
if we don’t get enough suggestion switch to
non personalised (also for non logged users)
The end?
There’s more...
https://neo4j.com/developer/guide-build-a-recommendation-engine/
Do it on your own
https://github.com/grahamjenson/ger
https://github.com/grahamjenson/list_of_recommender_systems
Links
http://www.slideshare.net/NYCPredictiveAnalytics/building-a-recommendation-engine-an-example-of-a-product-recommendation-engine?next_slideshow=1
https://www.coursera.org/learn/recommender-systems-introduction
http://actionml.com/
https://github.com/grahamjenson/ger
http://www.emailvendorselection.com/recommendation-engines-for-email-marketing/
https://blog.mozilla.org/ux/2014/04/the-experience-of-mind-reading/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
www.slideshare.net/treygrainger/building-a-real-time-solrpowered-recommendation-engine
Michele Orselli
CTO@Ideato
_orso_
micheleorselli / ideatosrl
mo@ideato.it

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