7RecommendationSystems - CS345 Data Mining Recommendation...

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    CS345 Data Mining Recommendation Systems Netflix Challenge Anand Rajaraman, Jeffrey D. Ullman
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  Recommendations  Items Search Recommendations Products, web sites, blogs, news items, …
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  From scarcity to abundance Shelf space is a scarce commodity for  traditional retailers  Also: TV networks, movie theaters,… The web enables near-zero-cost  dissemination of information about products From scarcity to abundance More choice necessitates better filters Recommendation engines How  Into Thin Air  made  Touching the Void  a  bestseller
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  The Long Tail Source: Chris Anderson (2004)
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  Recommendation Types Editorial Simple aggregates Top 10, Most Popular, Recent Uploads Tailored to individual users Amazon, Netflix, …
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  Formal Model C  = set of Customers S  = set of Items Utility function  u C   £ S   !   R = set of ratings R  is a totally ordered set e.g., 0-5 stars, real number in [0,1]
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  Utility Matrix 0.4 1 0.2 0.3 0.5 0.2 1 King Kong King Kong LOTR LOTR Matrix Matrix Nacho Libre Nacho Libre Alice Alice Bob Bob Carol Carol David David
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  Key Problems Gathering “known” ratings for matrix Extrapolate unknown ratings from known  ratings Mainly interested in high unknown ratings Evaluating extrapolation methods
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  Gathering Ratings Explicit Ask people to rate items Doesn’t work well in practice – people can’t be  bothered Implicit Learn ratings from user actions e.g., purchase implies high rating What about low ratings?
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  Extrapolating Utilities Key problem: matrix U is sparse most people have not rated most items Three approaches Content-based Collaborative Hybrid
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7RecommendationSystems - CS345 Data Mining Recommendation...

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