From Amazon, to Spotify, to thermostats, recommendation systems are everywhere. The ability to provide recommendations for your users is becoming a crucial feature for modern applications. In this talk I'll show you how you can use Ruby to build recommendation systems for your users. You don't need a PhD to build a simple recommendation engine -- all you need is Ruby. Together we'll dive into the dark arts of machine learning and you'll discover that writing a basic recommendation engine is not as hard as you might have imagined. Using Ruby I'll teach you some of the common algorithms used in recommender systems, such as: Collaborative Filtering, K-Nearest Neighbor, and Pearson Correlation Coefficient. At the end of the talk you should be on your way to writing your own basic recommendation system in Ruby.

1. People who liked this talk also liked …
Building Recommendation Systems
Using Ruby
Ryan Weald, @rweald
LA RubyConf 2013
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2. Who is this guy?
What does he know
about recommendation
systems?
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3. Data Scientist @Sharethrough
Native advertising
platform
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5. Outline
1) What is a recommendation system?
2) Collaborative filtering based
recommendations
3) Content based recommendations
4) Hybrid systems - the best of both worlds
5) Evaluating your recommendation system
6) Resources & existing libraries
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6. What this Talk is Not
• Everything there is to know about
recommendation systems.
• Bleeding edge machine learning
• How to use a specific library
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7. What is a
recommendation system?
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8. A program that predicts
a user’s preferences using information
about the user, other users, and the
items in your system.
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9. LinkedIn
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10. Netflix
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11. Spotify
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12. Amazon
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13. How do I build
recommendations?
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14. Two Main Categories of Algorithm
1. Collaborative Filtering (CF)
2. Content Based - Classification
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15. Collaborative Filtering
Fill in missing user preferences using
similar users or items
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16. Two Types of CF
1. Memory Based - Uses similarity
between users or items. Dataset
usually kept in memory
2. Model Based - Model generated
to “explain” observed ratings
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17. User Based CF
(User x Item) Matrix + Similarity
Function = Top-K most similar users
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18. Collaborative Filtering
Video 1 Video 2 Video 3 Video 4 Video 5
User 1 0 1 0 5 0
User 2 1 2 1 0 5
User 3 2 5 0 0 2
User 4 5 4 4 1 1
User 5 2 4 2
? ?
* 0 denotes not rated
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19. Similarity Functions
• Pearson Correlation Coefficient
• Cosine Similarity
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20. Pearson Correlation Coefficient
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21. Calculating PCC
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22. Calculating PCC
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23. Calculating PCC
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24. Calculating PCC
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25. Calculating PCC
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26. Calculating PCC
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27. 27

28. Using similarity to
recommend items
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29. Collaborative Filtering
Video 1 Video 2 Video 3 Video 4 Video 5
User 1 0 1 0 5 0
User 2 1 2 1 0 5
User 3 2 5 0 0 2
User 4 5 4 4 1 1
User 5 2 4 2
? ?
* 0 denotes not rated
29

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31. Problems With CF
• Cold Start
• Data Sparsity
• Resource expensive
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32. Doesn’t the video
content matter for
recommendations?
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33. Content Based Recommendations
Classify items based on features of
the item. Pick other items from
same class to recommend.
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34. Content Based Algorithms
• K-means clustering
• Random Forrest
• Support Vector Machines
• ...
• Insert your favorite ML algorithm
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35. Content Based Algorithms
Type of Duration Maturity
content Rating
Video 1 comedy 60 G
Video 2 action 120 G
Video 3 comedy 34 PG-13
Video 4 romantic 15 R
Video 5 sports 120 G
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36. K-means Clustering
Group items into K clusters.
Assign new item to a cluster and
pick items from that cluster
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37. K-means Clustering
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38. Problems With Content Based
Recommendations
• Unsupervised Learning is hard
• Training data limited or expensive
• Doesn’t take user into account
• Limited by features of content
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39. Hybrid Recommendations
Combine collaborative filtering with
content based algorithm to achieve
greater results
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40. Hybrid Recommendations
Input
CF Based
Recommender
Combiner Reco
Input
Content Based
Recommender
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41. Hybrid Recommendations
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42. Hybrid Recommendations
Content CF
Input Reco
Recommender Recommender
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43. Hybrid Recommendations
CF
Recommender
Input Reco
Content
Recommender
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44. Evaluating Recommendation Quality
• Precision vs. Recall
• Clicks
• Click through rate
• Direct user feedback
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45. Precision vs. Recall
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46. Precision vs. Recall
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47. Summary of What We’ve Learned
• Collaborative Filtering using similar users
• Content clustering using k-means
• Combining 2 algorithms to boost quality
• How to evaluate your recommender
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48. Don’t Reinvent the Wheel
• Apache Mahout
• JRuby mahout gem
• SciRuby
• Recommenderlab for R
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49. Resources & Further Reading
• Recommender Systems: An Introduction
• Linden, Greg, Brent Smith, and Jeremy York.
"Amazon. com recommendations: Item-to-item
collaborative filtering."
• Resnick, Paul, et al. "GroupLens: an open architecture
for collaborative filtering of netnews."
• ACM RecSys Conference Proceedings
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50. We’re Hiring
http://bit.ly/str-engineering
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51. Thanks!
Twitter: @rweald
Email: ryan@sharethrough.com
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