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Mini-training: Personalization & Recommendation Demystified

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Recommendations are everywhere : music, movies, books, social medias, e-commerce web sites… The Web is leaving the era of search and entering one of discovery. This quick introduction will help you to understand this vast topic and why you should use it.

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Mini-training: Personalization & Recommendation Demystified

  1. 1. PERSONALIZATION & RECOMMENDATION DEMYSTIFIED PEOPLE WHO READ THIS PRESENTATION ALSO READ …. MAXIME LEMAITRE – 03/07/14
  2. 2. Agenda • Introduction • Brief History • Paradigms • An example • This is not ended Recommender/recommendation systems/engines are a subclass of information filtering system that seek to predict the rating or preference that user would give to an item
  3. 3. Recommendations are everywhere Movies, Social, Books, Music, News …
  4. 4. Recommendations are everywhere Commons requirements, many usages An online music service with 20 millions of songs … 10 millions of users … How to recommend – pertinent- music to each user ?
  5. 5. Drive Traffic A recommendation engine can bring traffic to your site. (with personalized email messages and targeted blasts) Deliver Relevant Content By analyzing the customer’s current site usage and his previous browsing history, a recommendation engine can deliver relevant product recommendations as he shops. The data is collected in real-time so the software can react as his shopping habits change. Engage Shoppers Shoppers become more engaged in the site when personalized product recommendations are made. They are able to delve more deeply into the product line without having to perform search after search. Convert Shoppers to Customers Converting shoppers into customers takes a special touch. Personalized interactions from a recommendation engine show your customer that he is valued as an individual. In turn, this engenders his loyalty. Reduce Workload and Overhead Using an engine automates creation of a personal shopping experience, easing the workload of your IT staff and your budget. 5 Recommendation System Benefits (TL;DR) Increase Order Value / Number of Items per Order Average order values typically go up when a recommendation engine in uses to display personalized options. Advanced metrics and reporting can definitively show the effectiveness of a campaign. When the customer is shown options that meet his interest, he is more likely to add items to his purchase. Control Merchandising and Inventory Rules A recommendation engine can add your own marketing and inventory control directives to the customer’s profile to feature products that are promotionally prices, on clearance or overstocked. It gives you’re the flexibility to control what items are highlighted by the recommendation system. Provide Reports Providing reports is an integral part of a personalization system. Giving the client accurate and up to the minute reporting allows him to make solid decisions about his site and the direction of a campaign. Offer Advice and Direction An experienced provider can offer advice on how to use the data collected and reported to the client. Acting as a partner and a consultant, the provider should have the know-how to help guide the ecommerce site to a prosperous future.
  6. 6. 6 A brief History Recommenders are older than you might think 1999-2000 • The introduction and vast success of the Amazon recommendation engine in the early 2000s led to wide acceptance of the technology as a way of increasing sales Late 1970s • Recommendation systems have their roots in Usenet, a worldwide distributed discussion system originating at Duke University 2006 •Netflix Prize Boosted researches in this area Early 2000s • In addition to Amazon, many companies make recommendations a core value add of their services Late 2000s • Big Data. How to build large scale & real-time recommendation engines ?
  7. 7. The Netflix Prize http://www.netflixprize.com/ “a $1 million prize for improving Netflix recommendations by 10%” • Netflix is an online DVD-rental service • Recommendation algorithm is the core of their business. – Their whole business model is around cross selling products (movies) to consumers – The better it works, the more money they stand to make. • Netflix's own algorithm is called Cinematch • About the Data : 100,480,507 ratings that 480,189 users gave to 17,770 movies • Won in 2009, but was a fantastic booster for this area Recommender system is an active research area in the data mining and machine learning areas. Some conferences such as RecSys, SIGIR, KDD have it as a topic…
  8. 8. “The Web, they say, is leaving the era of search and entering one of discovery. What's the difference? Search is what you do when you're looking for something. Discovery is when something wonderful that you didn't know existed, or didn't know how to ask for, finds you.”, Fortune Magazine 8 Recommendation != Search Engine Recommendation Engine Predict how much a user will like an item that is unknown for him/her based on context, preferences, friends, similarity, location, … DISCOVER Search Engine Index and retrieve by criteria similar documents based exclusively on content FIND ( But search is starting to take user into account … )
  9. 9. Recommendations are just ranked list for a user 9 Recommendation as a dedicated function Item A Item A Item A Item A Item A Items Item X Item Y Item Z Recommendation Engine Item A Item A Item A Item A Item A Users User A Most of recommender systems are capable of accurately recommending complex items without requiring an "understanding" of the item itself
  10. 10. • Collaborative filtering filtering methods based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users • Content-based filtering filtering methods based on a description of the item and a profile of the user’s preference. Keywords/Meta are used to describe the items; beside, a user profile is built to indicate the type of item this user likes • Hybrid Recommender Systems Mix collaborative filtering and content-based filtering in several ways ; it could be more effective in some cases 10 Paradigms
  11. 11. • The most prominent approach to generate recommendations – used by large, commercial e‐commerce sites – well‐understood, various algorithms and variations exist – applicable in many domains (book, movies, DVDs, ..) • Approach – use the "wisdom of the crowd" to recommend items • Basic assumption and idea – Users give ratings to catalog items (implicitly or explicitly) – Customers who had similar tastes in the past, will have similar tastes in the fu ture 11 Paradigms – Collaborative Filtering The most prominent approach to generate recommendations
  12. 12. Paradigms – Collaborative Filtering Plethora of different techniques proposed in the last years • Memory‐based approaches – the rating matrix is directly used to find neighbors / make predictions – does not scale for most real‐world scenarios – large e‐commerce sites have tens of millions of customers and millions of ite ms Ex : kNN, Slope One … • Model‐based approaches – based on an offline pre‐processing or "model‐learning" phase – at run‐time, only the learned model is used to make predictions – models are updated / re‐trained periodically – large variety of techniques used – model‐building and updating can be computationally expensive Ex : Matrix Factorization (SVD), clustering models, Bayesian networks, probabilistic Latent Semantic Analysis , … 12
  13. 13. 13 Neighborhood-based Collaborative Filtering
  14. 14. 14 User-based Collaborative Filtering (1/6)
  15. 15. 15 User-based Collaborative Filtering (2/6) Dimensions Vectors
  16. 16. 16 User-based Collaborative Filtering (3/6)
  17. 17. 17 User-based Collaborative Filtering (4/6)
  18. 18. 18 User-based Collaborative Filtering (5/6)
  19. 19. 19 User-based Collaborative filtering (6/6) Items Bought By User1
  20. 20. • Sparse data Most users do not rate implicitly/explicitly most items. Less data means recommendations may be irrelevant. • Scalability CF algorithms computation time grows with the number of items and users. Big data processing requires dedicated infrastructures & components (Hadoop, MapReduce, HDInsight, Cloud, …) • Cold Start Require a large amount of existing data on a user in order to make accurate recommendations. New users/items to information to leverage. – New user : never gave feedbacks – New item : never rated 20 Collaborative filtering Challenges and issues
  21. 21. • Evaluating Recommender Systems – Is a RS efficient with respect to a specific criteria like accuracy, user satisfaction, response time, serendipity, online conversion, … – Do customers like/buy recommended items? – Do customers buy items they otherwise would have not? – Are they satisfied with a recommendation after purchase? 21 The is not the end Let data speak for itself Netflix’s workflow
  22. 22. 22 Make sure it is needed ACM Conference, Barcelona, 2010
  23. 23. Questions
  24. 24. References • http://en.wikipedia.org/wiki/Recommender_system • http://en.wikipedia.org/wiki/Collaborative_filtering • http://en.wikipedia.org/wiki/Slope_One • http://www.slideshare.net/ErnestoMislej/recommender-systems-asai-2011 • http://www.slideshare.net/torrens/top-10-lessons-learned-developing-deploying-and-operating-realworld- recommender-systems-5351028 • http://www.recsyswiki.com/wiki/Main_Page • http://www.slideshare.net/WorapotJakkhupan/basic-of-recommender-system • http://pkghosh.wordpress.com/2010/10/19/recommendation-engine-powered-by-hadoop-part-1/ • http://web4.cs.ucl.ac.uk/staff/jun.wang/blog/topics/research-resources/collaborative-filtering/ • http://techblog.netflix.com/2012/06/netflix-recommendations-beyond-5-stars.html • http://www.hindawi.com/journals/aai/2009/421425/ • http://www.certona.com/recommendation-software/benefit-of-recommendation-engines • http://www.recommenderbook.net/teaching-material • http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system • http://www.slideshare.net/kerveros99/essir-2013-recsysfinal-25957057 • https://github.com/neo4j-contrib/graphgist/wiki
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