This document explains the concept and workings of recommendation engines, which help users find relevant items amidst vast choices, contrasting them with traditional search engines. It details personalized recommendations through content-based and collaborative filtering methods, while addressing issues such as user and item cold starts and sparsity. The document also introduces implementation strategies using PredictionIO for building custom recommendation systems.

2. A recommendation
engine
for your apps

4. 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

5. So… it’s a search engine!

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

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

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

9. Classification

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

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

12. Personalisation levels
• Non personalised: best sellers
• Demographic: age, location
• Ephemeral: based on current activities
• Persistent

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

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

15. A model for comparison

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

18. Non personalised

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

22. Hotel

23. Visitor Hotel

24. Visitor Hotel

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

26. Content based

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

29. 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

30. Problems/Limitations

31. 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

32. Collaborative filtering

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

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

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. 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?

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

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

39. Problems/Limitations

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

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

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

43. Scalability
With millions of ratings, computations
become slow
Dimensionality reduction FTW!

44. An example

45. 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?

46. 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

47. 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

48. Similarity
Bob 0.75
Alice 2
Tom 4.3
D = 1 / 1 + d

49. Similarity
Bob 1.57
Alice 0.33
Tom 0.18
D = 1 / 1 + d

50. 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.

51. 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

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

54. Our domain

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

56. Choosing the tool

57. PredictionIO

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

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

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

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

63. The event server

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

66. $ 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

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

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

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

73. 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)

74. Engines

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

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

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

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

80. Getting recommendations

84. Implementation

86. 2 kind of suggestions
- who bought this also bought
(recommendation)
- you may like (similarities)

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

88. 4 engines
2 for books, 2 for ebooks
(not needed now)
Retrained every night with new data

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

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

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

94. Alternative approaches

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

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

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

99. Michele Orselli
CTO@Ideato
_orso_
micheleorselli / ideatosrl
mo@ideato.it

100. 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