This document provides an overview of building a recommendation engine for an online book shop using PredictionIO. It discusses recommendation systems and different types of algorithms like content-based filtering, collaborative filtering using user-user and item-item similarities, and model-based approaches. It also covers installing and using PredictionIO, modeling event data, building recommendation engines, and implementing the engine to provide book and ebook recommendations to users based on actions like views, likes and purchases.

1. A recommendation
engine
for your PHP apps

2. 1) Intro to recommender systems
2) PredictionIO
3) Case Study

3. Definition: a system that help people
finding things when the process of
finding what you need is challenging
because you have a lot of choices/
alternatives

4. So… it’s a search engine!

5. Search Engines
Document base is (almost) static
Queries are dynamic

6. Search Engines
Create an index analyzing the
documents
Calculate relevance for a query: tf*idf

7. Recommender systems
Document base is growing (eg: Netflix)
Query is static: find something I like

8. Classification

9. Domain: news, products, …
Helps defining what can be suggested

10. Purpose: sales, information, education,
build a community
What is TripAdvisor purpose?

11. Personalization levels
• Non personalized: best sellers
• Demographic: age, location
• Ephemeral: based on current activities
• Persistent

12. Types of input
• Explicit: ask user to rate something
• Implicit: inferred from user behaviour

13. Output
• Prediction: predicted rating,
evaluation
• Recommendations: suggestion list,
top-n, offers, promotion
• Filtering: email filters, news articles

14. A model for comparison

15. User: people with preference
Items: subject of rating
Rating: expression of opinion
(Community: space where opinions
makes sense)

17. Non-personalized

18. Best seller
Most popular
Trending
Summary of community ratings: eg best
hotel in town

21. Hotel

22. Visitor Hotel

23. Visitor Hotel

24. Hotel A Hotel B Hotel C
John 3 5
Jane 3
Fred 1 0
Tom 4
AVG 3.5 3 0

25. Content based

26. User rate items
We build a model of user preference
Look for similar items based on the
model

28. Action 0.7
Sci Fi 3.2
Vin Diesel 1.2
… …
https://www.amazon.com/Relevant-Search-applications-Solr-Elasticsearch/dp/161729277X
http://www.slideshare.net/treygrainger/building-a-real-time-solrpowered-recommendation-engine

29. Problems/Limitations

30. 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

31. Collaborative filtering

32. No need to analyze (index) content
Can capture more subtle things
Serendipity

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

34. 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?

35. 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?

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

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

38. Problems/Limitations

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

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

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

42. Scalability
With millions of ratings, computations
become slow

43. Dimensionality reduction

44. Express my opinions as a set of tastes
Compact representation of the matrix
with relevant features

45. Rogue
One
1
3
5
Joe 1 2 3

46. An example

47. 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?

48. Only consider items both users have rated
For each item
- Calculate 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

49. Sim(Joe, Tom) = (|8-2| + |2-7| + |7-5|)/3 = 13/3 = 4.3
Sim(Alice, Bob) = (|5-7| + |4-1| + |4-3| + |7-8|)/4 =
7/4 = 1.75
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

50. Now we have a score or weight for
each user

51. 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.

52. 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

53. 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 Item1 and Item2?
How similar are Item1 and Item3?

54. Only consider items both users have rated
For each item
- Calculate difference in ratings for the 2 items
- Take the average of this difference over the users
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

55. Sim(I1, I2) = (|8-1| + |5-4| + |7-1|)/3 = 14/3 = 4,6
Sim(I1, I3) = (|2-5| + |5-7| + |7-7|)/3 = 5/3 = 1,6
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

56. As user-user, use whole matrix or identify neighbors

57. Cosine similarity
[3,5]
[2,7]
[0,0]

58. Our domain

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

60. Choosing the tool

61. PredictionIO

62. Under the Apache umbrella
Based on solid open source stack
Customizable templates engines
SDK for PHP

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

65. Installation via source code
http://predictionio.incubator.apache.org/
install/install-sourcecode/

66. You can choose storage
mysql/postgres vs elasticsearch+hbase

67. The event server

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

70. $ 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

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

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

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

77. $set , $unset and $delete
_pio* are reserved

78. 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)

79. Engines

81. D.A.S.E Architecture
Data Source and Preparation
Algorithm
Serving
Evaluation

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

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

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

85. Getting recommendations

89. Implementation

91. 2 kind of suggestions
- who bought this also bought
(recommendation)
- you may like (similarities)

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

93. 4 engines
2 for books, 2 for ebooks
(not needed now)
Retrained every night with new data

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

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

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

99. user cold start/item cold start
if we don’t get enough suggestion
switch to non personalized (best
sellers)

100. Michele Orselli
CTO@Ideato
_orso_
micheleorselli / ideatosrl
mo@ideato.it
https://joind.in/talk/93d2d

101. 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