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How to Build a Recommendation Engine on Spark

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How to Build a Recommendation Engine on Spark was a presentation given by Joe Caserta, CEO and founder of Caserta Concepts, at @AnalyticsWeek in Boston.

Boston's Data AnalyticsStreet Conference is a 2 day packed event with thought provoking keynotes, knowledge filled sessions, intense workshops, insightful panels, and real-world case studies - engaging analytics community with latest methodologies and trends. The conference encompasses largest Speaker-to-Attendee ratio for unmatched networking and learning opportunity.

For more information on the services and solutions Caserta Concepts offers, visit our website at http://casertaconcepts.com/.

Published in: Technology

How to Build a Recommendation Engine on Spark

  1. 1. #AnalyticsStreet @joe_Caserta Building a Recommendation Engine on Spark Joe Caserta President, Caserta Concepts joe@casertaconcepts.com (914) 261-3648 @joe_Caserta
  2. 2. About Caserta Concepts • Technology services company with expertise in data analysis: • Big Data Solutions • Data Warehousing • Business Intelligence • Data Science & Analytics • Data on the Cloud • Data Interaction & Visualization • Core focus in the following industries: • eCommerce / Retail / Marketing • Financial Services / Insurance • Healthcare / Ad Tech / Higher Ed • Established in 2001: • Increased growth year-over-year • Industry recognized work force • Strategy, Implementation • Writing, Education, Mentoring #AnalyticsStreet @joe_Caserta
  3. 3. Why Big Data? Enrollments Claims Finance ETL Traditional EDW Big Data Cluster #AnalyticsStreet @joe_Caserta Big Data Analytics Ad-Hoc Query Traditional BI Horizontally Scalable Environment - Optimized for Analytics Canned Reporting NoSQL Databases ETL Ad-Hoc/Canned Reporting Spark MapReduce Pig/Hive N1 N2 N3 N4 N5 Hadoop Distributed File System (HDFS) Others… Data Science
  4. 4. What is Spark • Spark is a fast, general-purpose cluster computing framework. • Sits on top of Hadoop • Up to 100 times faster than Map Reduce • In-memory cluster computing – well suited for machine learning • Provides high-level APIs in Java, Scala and Python. Tools include: • Spark SQL • MLlib • GraphX Data Science Training: • Spark Streaming https://exploredatascience.com/ #AnalyticsStreet @joe_Caserta
  5. 5. Project Objective • Create a functional recommendation engine to surface to provide relevant product recommendations to customers. • Improve Customer Experience • Increase Customer Retention • Increase Customer Purchase Activity • Establish Hadoop with Spark as a high performance, scalable solution for computing and storage • Accurately suggest relevant products to customers based on their peer behavior. Integrate existing EDW data with Hadoop natively using an enterprise class ETL tool • Implement an enterprise class business intelligence tool sourcing directly from Hadoop #AnalyticsStreet @joe_Caserta
  6. 6. Hadoop Environment • Lab Setup • 10 node cluster - Cloudera • 1 TB under management with inexpensive commodity hardware • ETL – Talend • Load data from Enterprise Data Warehouse into Hadoop • Efficacy Reporting - Datameer • Recommendation Engine Built and Tested • Recommendations are as good or better than anticipated • More relevant than possible without Big Data solution • Algorithms can easily be fine-tuned by adjusting: • The number of recommendations in the results • The weighting of the relevancy of the Product #AnalyticsStreet @joe_Caserta
  7. 7. The Math Behind Relevance • Finding ‘Similar’ Objects Cosine Similarity  • Value of cos θ varies between: Figure. Vectors A & B • -1 [‘θ’ = 180◦, Absolutely dissimilar – Opposite ended vectors/relationship] • 0 [‘θ’ = 90◦, Dissimilar, perpendicular vectors/relationship] • +1 [‘θ’ = 0◦, Absolutely Similar – Overlapping vectors/relationship] #AnalyticsStreet @joe_Caserta
  8. 8. Recommendations • Your customers expect them • Good recommendations make life easier • Help them find information, products, and services they might not have thought of • What makes a good recommendation? • Relevant but not obvious • Sense of “surprise” 23” LED TV 24” LED TV 25” LED TV SOLD!! 23” LED TV`` Blu-Ray Home Theater HDMI Cables #AnalyticsStreet @joe_Caserta
  9. 9. Where do we use recommendations? • Applications can be found in a wide variety of industries and applications: • Travel • Financial Service • Music/Online radio • TV and Video • Online Publications • Retail ..and countless others Our Example: Movies #AnalyticsStreet @joe_Caserta
  10. 10. Our Goal • Create a powerful, scalable recommendation engine with minimal development • Make recommendations to users as they are browsing movie titles - instantaneously • Recommendation must have context to the movie they are currently viewing. OOPS! – too much surprise! #AnalyticsStreet @joe_Caserta
  11. 11. How do we do it? Hadoop – distributed file system and processing platform Spark – low-latency computing MLlib – Library of Machine Learning Algorithms We leverage two algorithms: • Content-Based Filtering – how similar is this particular movie to other movies based on usage. • Collaborative Filtering – predict an individuals preference based on their peers ratings. Spark MLlib implements a collaborative filtering algorithm called Alternating Least Squares (ALS) • Both algorithms only require a simple dataset of 3 fields: “User ID” , “Item ID”, “Rating” #AnalyticsStreet @joe_Caserta
  12. 12. Content-Based Filtering “People who liked this movie liked these as well” • Content Based Filter builds a matrix of items to other items and calculates similarity (based on user rating) • The most similar item are then output as a list: • Item ID, Similar Item ID, Similarity Score • Items with the highest score are most similar • In this example users who liked “Twelve Monkeys” (7) also like “Fargo” (100) 7 100 0.690951001800917 7 50 0.653299445638532 7 117 0.643701303640083 At the moment, content based filtering is not available for Spark in Mllib. On our project, we used Mahout. #AnalyticsStreet @joe_Caserta
  13. 13. Collaborative Filtering “People with similar taste to you liked these movies” • Collaborative filtering applies weights based on “peer” user preference. • Essentially it determines the best movie critics for you to follow • The items with the highest recommendation score are then output as tuples • User ID [Item ID1:Score,…., Item IDn:Score] • Items with the highest recommendation score are the most relevant to this user • For user “Johny Sisklebert” (572), the two most highly recommended movies are “Seven” and “Donnie Brasco” 572 [11:5.0,293:4.70718,8:4.688335,273:4.687676,427:4.685926,234:4.683155,168:4.669672,89:4.66959,4:4.65515] 573 [487:4.54397,1203:4.5291,616:4.51644,605:4.49344,709:4.3406,502:4.33706,152:4.32263,503:4.20515,432:4.26455,611:4.22019] 574 [1:5.0,902:5.0,546:5.0,13:5.0,534:5.0,533:5.0,531:5.0,1082:5.0,1631:5.0,515:5.0] #AnalyticsStreet @joe_Caserta
  14. 14. Recommendation Store • Serving recommendations needs to be instantaneous • The core to this solution is two reference tables: Rec_Item_Similarity Item_ID Similar_Item Similarity_Score Rec_User_Item_Base User_ID Item_ID Recommendation_Score • When called to make recommendations we query our store • Rec_Item_Similarity based on the Item_ID they are viewing • Rec_User_Item_Base based on their User_ID #AnalyticsStreet @joe_Caserta
  15. 15. Delivering Recommendations So if Johny is viewing “12 Monkeys” we query our recommendation store and present the results #AnalyticsStreet @joe_Caserta Item-Based: Peers like these Movies Best Recommendations Item Similarity Raw Score Score Fargo 0.691 1.000 Star Wars 0.653 0.946 Rock, The 0.644 0.932 Pulp Fiction 0.628 0.909 Return of the Jedi 0.627 0.908 Independence Day 0.618 0.894 Willy Wonka 0.603 0.872 Mission: Impossible 0.597 0.864 Silence of the Lambs, The 0.596 0.863 Star Trek: First Contact 0.594 0.859 Raiders of the Lost Ark 0.584 0.845 Terminator, The 0.574 0.831 Blade Runner 0.571 0.826 Usual Suspects, The 0.569 0.823 Seven (Se7en) 0.569 0.823 Item-Base (Peer) Raw Score Score Seven 5.000 1.000 Donnie Brasco 4.707 0.941 Babe 4.688 0.938 Heat 4.688 0.938 To Kill a Mockingbird 4.686 0.937 Jaws 4.683 0.937 Monty Python, Holy Grail 4.670 0.934 Blade Runner 4.670 0.934 Get Shorty 4.655 0.931 Top 10 Recommendations Seven (Se7en) 1.823 Blade Runner 1.760 Fargo 1.000 Star Wars 0.946 Donnie Brasco 0.941 Babe 0.938 Heat 0.938 To Kill a Mockingbird 0.937 Jaws 0.937 Monty Python, Holy Grail 0.934
  16. 16. From Good to Great Recommendations • Note that the first 5 recommendations look pretty good …but the 6th result would have been “Babe” the children's movie • Tuning the algorithms might help: parameter changes, similarity measures. • How else can we make it better? 1. Delivery filters 2. Introduce additional algorithms such as K-Means #AnalyticsStreet @joe_Caserta OOPS!
  17. 17. Additional Algorithm – K-Means “These movies are similar based on their attributes” • Treats items as coordinates • Places a number of random “centroids” and assigns the nearest items • Moves the centroids around based on average location • Process repeats until the assignments stop changing We would use the major attributes of the Movie to create coordinate points. • Categories • Actors • Director • Synopsis Text #AnalyticsStreet @joe_Caserta
  18. 18. Delivery Scoring and Filters Apply assumptions to control the results of collaborative filtering • One or more categories must match • Only children movies will be recommended for children's movies. Action Adventure Children's Comedy Crime Drama Film-Noir Horror Romance Sci-Fi Thriller Twelve Monkeys 0 0 0 0 0 1 0 0 0 1 0 Babe 0 0 1 1 0 1 0 0 0 0 0 Seven (Se7en) 0 0 0 0 1 1 0 0 0 0 1 Star Wars 1 1 0 0 0 0 0 0 1 1 0 Blade Runner 0 0 0 0 0 0 1 0 0 1 0 Fargo 0 0 0 0 1 1 0 0 0 0 1 Willy Wonka 0 1 1 1 0 0 0 0 0 0 0 Monty Python 0 0 0 1 0 0 0 0 0 0 0 Jaws 1 0 0 0 0 0 0 1 0 0 0 Heat 1 0 0 0 1 0 0 0 0 0 1 Donnie Brasco 0 0 0 0 1 1 0 0 0 0 0 To Kill a Mockingbird 0 0 0 0 0 1 0 0 0 0 0 Similarly logic could be applied to promote more favorable options • New Releases • Retail Case: Items that are on-sale, overstock #AnalyticsStreet @joe_Caserta
  19. 19. Integrating K-Means into the process Movies recommended by more than 1 algorithm are the most highly rated Collaborative Filter K-Means: Similar Content Filter #AnalyticsStreet @joe_Caserta Best Recommendations
  20. 20. Sophisticated Recommendation Model 20 What are people with similar characteristics buying? #AnalyticsStreet @joe_Caserta What items are we promoting at time of sale? What items are being promoted by the Store or Market? 20 Peer Based Item Clustering Corporate Deals/ Offers Customer Behavior Market/ Store Recommendation What items have you bought in the past? What did people who ordered these items also order? The solution allows balancing of algorithms to attain the most effective recommendation
  21. 21. Summary • Hadoop and Spark can provide a relatively low cost and extremely scalable platform for recommendations • Spark, with MLlib offers a great library of established Machine Learning algorithms, reducing development efforts • A good recommendation system combines Collaborative and Content filtering algorithms and custom business rules • As Spark matures, Mahout or roll-your-own algorithms may be needed. #AnalyticsStreet @joe_Caserta
  22. 22. Thank You Joe Caserta President, Caserta Concepts joe@casertaconcepts.com (914) 261-3648 @joe_Caserta #AnalyticsStreet @joe_Caserta

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