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Recommender systems

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Anastasiia Kornilova
Anastasiia Kornilova
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Recommender Systems Anastasiia Kornilova
Agenda General overview Algorithms Evaluation Problems
We are overloaded of information: • Books • Movies • News • Blogs • TV-channels • Music •…
As user: • • • • • • Do we need all of this things? No Can we choose most appropriate of them? Yes How? Recommender systems!
As Business Owner: Do you need Recommender System? • Netflix: • • Google News • • 2/3 rented movies are from recommendation 38 % more click-through are due to recommendation Amazon • 35% sales are from recommendation •Celma & Lamere, ISMIR 2007
Domains of recommendations Content to Commerce • • • • • • • • One particularly interesting property • New items (movies, books, news, ..) • Re-recommend old ones (groceries, music,…) Information News Restaurants Vendors TV-programs Courses in e-learning People Music playlists
Examples: Retail
Examples: Banking Deposits: Credit products: Insurance: Service packages: Deposit 1 Credit card 1 Endowment Premium package Deposit 2 Credit card 2 Travel insurance Personal loan 1 Personal loan 2 Consultant can be replaced by Recommender System Deposit 1 Personal load 2 Travel insurance Travel insurance Customer 1 Credit card 1 Premium package Customer 2
Examples: Hotels
Examples: Advertisement Shopping (browsing) history RSE
Purposes of Recommendation Recommendations themselves (Sales, information) Education of user/customer Build a community of users/customers around products or content
Whose Opinion?    “Experts” Ordinary “phoaks” People like you
Personalization Level Generic/Non-Personalized: everyone receives same recommendations Demographic: matches a target group Ephemeral: matches current activity Persistent: matches long-term interests
Review Rating Explicit input based RSE Vote Like
Purchase Click Follow Implicit input based RSE
Recommendation Algorithms 1. Non-Personalized Summary Statistics 2. Content-Based Filtering Information Filtering  Knowledge-Based 3. Collaborative Filtering  User-user  Item-item  Dimensionality Reduction 4. Others  Critique / Interview Based Recommendations  Hybrid Techniques 
Non-Personalized Recommender Best-seller Most popular Trending Hot Best-liked People who X also Y
Personalized Recommender: Collaborative Filtering     Use opinions of others to predict/recommend User model – set of ratings Item model – set of ratings Common core: sparse matrix of ratings
Evaluation Lift Cross-sales Up-sales Conversions Accuracy Serendipity
Problems New user “Cold start” New item New system

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