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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.
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People who liked this talk also liked … Building Recommendation Systems
Using Ruby
Ryan Weald, @rwealdLA RubyConf 2013
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Who is this guy?
What does he know about recommendation
systems?
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Data Scientist @Sharethrough
Native advertising platform
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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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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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What is a recommendation system?
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A program that predictsa user’s preferences using information about the user, other users, and the
items in your system.
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Netflix
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Spotify
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Amazon
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How do I build recommendations?
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Two Main Categories of Algorithm
1. Collaborative Filtering (CF)
2. Content Based - Classification
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Collaborative Filtering
Fill in missing user preferences using similar users or items
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1. Memory Based - Uses similarity between users or items. Dataset usually kept in memory
2. Model Based - Model generated to “explain” observed ratings
Two Types of CF
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(User x Item) Matrix + Similarity Function = Top-K most similar users
User Based CF
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Video 1 Video 2 Video 3 Video 4 Video 5
User 1
User 2
User 3
User 4
User 5
0 1 0 5 0
1 2 1 0 5
2 5 0 0 2
5 4 4 1 1
2 4 ? ? 2
Collaborative Filtering
* 0 denotes not rated
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Similarity Functions
• Pearson Correlation Coefficient
• Cosine Similarity
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Pearson Correlation Coefficient
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Calculating PCC
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Calculating PCC
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Calculating PCC
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Calculating PCC
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Calculating PCC
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Calculating PCC
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Using similarity to recommend items
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Video 1 Video 2 Video 3 Video 4 Video 5
User 1
User 2
User 3
User 4
User 5
0 1 0 5 0
1 2 1 0 5
2 5 0 0 2
5 4 4 1 1
2 4 ? ? 2
Collaborative Filtering
* 0 denotes not rated
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• Cold Start
• Data Sparsity
• Resource expensive
Problems With CF
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Doesn’t the video content matter for recommendations?
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Content Based Recommendations
Classify items based on features of the item. Pick other items from
same class to recommend.
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Content Based Algorithms
• K-means clustering
• Random Forrest
• Support Vector Machines
• ...
• Insert your favorite ML algorithm
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Content Based AlgorithmsType ofcontent
Duration MaturityRating
Video 1
Video 2
Video 3
Video 4
Video 5
comedy 60 G
action 120 G
comedy 34 PG-13
romantic 15 R
sports 120 G
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K-means Clustering
Group items into K clusters. Assign new item to a cluster and
pick items from that cluster
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K-means Clustering
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• Unsupervised Learning is hard
• Training data limited or expensive
• Doesn’t take user into account
• Limited by features of content
Problems With Content Based Recommendations
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Hybrid Recommendations
Combine collaborative filtering with content based algorithm to achieve
greater results
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Hybrid Recommendations
Content Based Recommender
CF BasedRecommender
Combiner Reco
Input
Input
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Hybrid Recommendations
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Hybrid Recommendations
InputCF
RecommenderContent
RecommenderReco
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Hybrid Recommendations
CFRecommender
Content Recommender
Input Reco
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Evaluating Recommendation Quality
• Precision vs. Recall
• Clicks
• Click through rate
• Direct user feedback
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Precision vs. Recall
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Precision vs. Recall
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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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Don’t Reinvent the Wheel
• Apache Mahout
• JRuby mahout gem
• SciRuby
• Recommenderlab for R
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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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We’re Hiringhttp://bit.ly/str-engineering
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