This document discusses building a recommendation engine for a PHP application using PredictionIO. It covers installing PredictionIO, modeling event data, creating recommendation engines, and implementing functions for recording user actions and getting recommendations. Key aspects include modeling view, like, and purchase events, handling cold starts for users and items, and retraining engines daily with new data to provide personalized book and ebook recommendations.

1. A recommendation
engine
for your
PHP apps

3. Search Engines

4. Recommender systems

5. Non personalised

6. Content based

7. Collaborative
filtering

8. No need to analyse (index) content
Can capture more subtle things
Serendipity

9. User-User
Select people of my neighbourhood with
similar taste. If other people share my taste I
want their opinion combined

10. E.T
2 4
Joe 2 2 3 ?
1 5
5 2 4 …
Tom 3 3 2
4 1
User-User: which users have similar tastes?

11. E.T
2 4
Joe 2 2 3 ?
1 5
5 2 4 …
Tom 3 3 2
4 1
User-User: which users have similar tastes?

12. Item-Item
Find an items where I have expressed an
opinion and look how other people felt about
it. Precompute similarities between items

13. E.T
2 4
Joe 2 2 3 ?
1 5
5 2 4 …
Tom 3 3
4 1
Item-Item: which item are similar?

14. Problems/Limitations

15. Sparsity
When recommending from a large item set,
users will have rated only some of the items

16. User Cold start
Not enough known about new user to decide
who is similar

17. Item cold start
Cannot predict ratings for new item till some
similar users have rated it [No problem for
content-based]

18. Scalability
With millions of ratings, computations
become slow

19. Our domain

20. Domain: online book shop, both paper and
digital
Recommend titles, old and new
- Who bought this also bought
- You might like

21. Choosing the tool

22. PredictionIO

24. Installation
http://actionml.com/docs/pio_by_actionml
Pre-baked Amazon AMIs

25. The event server

27. Pattern: user -- action -- item
User 1 purchased product X
User 2 viewed product Y
User 1 added product Z in the cart

28. $ 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

29. Server runs on port 7070 by default
$ curl -i -X GET http://localhost:7070
{“status":"alive"}

30. $ curl -i -X GET “http://localhost:
7070/events.json?
accessKey=$ACCESS_KEY"

31. Events modeling
what can/should we model?
rate, like, buy, view, depending on the
algorithm

34. 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)

35. recordUserActionOnItem($event, $uid, $iid, array
$properties=array(), $eventTime=null)
createEvent(array $data)
getEvent($eventId)

36. Engines

38. $ pio template get apache/incubator-
predictionio-template-recommender
MyRecommendation
$ cd MyRecommendation

39. engine.json
"datasource": {
"params" : {
"appName": “MyApp1”,
"eventNames": [“buy”, “view”]
}
},

40. $ pio build —verbose
$ pio train
$ pio deploy

41. Getting
recommendations

45. Implementation

47. 2 kind of suggestions
- who bought this also bought
(recommendation)
- you may like (similarities)

48. View
Like (add to basket, add to wishlist)
Conversion (buy)
Recorded in batch

49. 4 engines
2 for books, 2 for ebooks
(not needed now)
Retrained every night with new data

50. recordLike($user, array $item)
recordConversion($user, array $item)
recordView($user, array $item)
createUser($uid)

51. getRecommendation($uid, $itype, $n=self::N_SUGGESTION)
getSimilarity($iid, $itype, $n=self::N_SUGGESTION)

53. user cold start/item cold start
if we don’t get enough suggestion switch to
non personalised (also for non logged users)

55. The end?

56. Alternative approaches

57. https://github.com/grahamjenson/list_of_recommender_systems

59. Do it on your own
https://github.com/grahamjenson/ger

60. https://neo4j.com/developer/guide-build-a-recommendation-engine/

61. Michele Orselli
CTO@Ideato
_orso_
micheleorselli / ideatosrl
mo@ideato.it
https://joind.in/talk/41ffc

62. 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