PERSONALIZATION & RECOMMENDATION DEMYSTIFIED

PEOPLE WHO READ THIS PRESENTATION ALSO READ ….MAXIME LEMAITRE – 03/07/14

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

Recommendations are everywhereMovies, Social, Books, Music, News …

An online music service with

20 millions of songs …

10 millions of users …

How to recommend –pertinent- music to each user ?

Recommendations are everywhereCommons requirements, many usages

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Drive TrafficA recommendation engine can bring traffic to your site. (with personalized email messages and targeted blasts)Deliver Relevant ContentBy 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 ShoppersShoppers 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 CustomersConverting 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 OverheadUsing an engine automates creation of a personal shopping experience, easing the workload of your IT staff and your budget.

Recommendation System Benefits (TL;DR)Increase Order Value / Number of Items per OrderAverage 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 RulesA 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 ReportsProviding 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 DirectionAn 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.

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A brief HistoryRecommenders are older than you might think

Late 1970s• Recommendation systems have their

roots in Usenet, a worldwide distributed discussion system originating at Duke University

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

Early 2000s• In addition to

Amazon, many companies make recommendations a core value add of their services

2006• Netflix Prize Boosted

researches in this area

Late 2000s• Big Data. How to build

large scale & real-time recommendation engines ?

The Netflix Prizehttp://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…

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“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

Recommendation != Search Engine

Recommendation EnginePredict how much a user will like an item that is unknown for him/her based on context, preferences, friends, similarity, location, …

DISCOVER

Search EngineIndex and retrieve by criteria similar documents based exclusively on content

FIND

( But search is starting to take user into account … )

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Recommendation as a dedicated function

Item AItem A

Item AItem A

Item AItems

Item X

Item Y

Item Z

Recommendations are just ranked list for a user

RecommendationEngine

Item AItem A

Item AItem A

Item AUsers

User A

Most of recommender systems are capable of accurately recommending complex items without requiring an "understanding" of the item itself

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• Collaborative filteringfiltering 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 filteringfiltering 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 SystemsMix collaborative filtering and content-based filtering in several ways ; it could be more effective in some cases

Paradigms

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• 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

future

Paradigms – Collaborative FilteringThe most prominent approach to generate recommendations

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• 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 ‐

itemsEx : 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 , …

Paradigms – Collaborative FilteringPlethora of different techniques proposed in the last years

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Neighborhood-based Collaborative Filtering

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User-based Collaborative Filtering (1/6)

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User-based Collaborative Filtering (2/6)Dimensions

Vect

ors

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User-based Collaborative Filtering (3/6)

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User-based Collaborative Filtering (4/6)

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User-based Collaborative Filtering (5/6)

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User-based Collaborative filtering (6/6)

Items Bought By User1

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• Sparse dataMost users do not rate implicitly/explicitly most items. Less data means recommendations may be irrelevant.

• ScalabilityCF algorithms computation time grows with the number of items and users. Big data processing requires dedicated infrastructures & components (Hadoop, MapReduce, HDInsight, Cloud, …)

• Cold StartRequire 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

Collaborative filteringChallenges and issues

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• 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?

The is not the endLet data speak for itself

Netflix’s workflow

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Make sure it is neededACM Conference, Barcelona, 2010

Questions

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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