08-recsys

08-recsys - CS246 Mining Massive Datasets Jure Leskovec...

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CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu
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Customer A Buys Metalica CD Buys Megadeth CD Customer B Does search on Metalica Recommender system suggests Megadeth from data collected from customer A
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Items Search Recommendations Products, web sites, blogs, news items, … 1/30/2011 3 Jure Leskovec, Stanford C246: Mining Massive Datasets Examples:
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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: http://www.wired.com/wired/archive/12.10/tail.html 1/30/2011 4 Jure Leskovec, Stanford C246: Mining Massive Datasets
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Source: Chris Anderson (2004) 1/30/2011 5 Jure Leskovec, Stanford C246: Mining Massive Datasets
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1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 6 Read http://www.wired.com/wired/archive/12.10/tail.html to learn more!
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Editorial Simple aggregates Top 10, Most Popular, Recent Uploads Tailored to individual users Amazon, Netflix, … 1/30/2011 7 Jure Leskovec, Stanford C246: Mining Massive Datasets
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C = set of Customers S = set of Items Utility function u : C × S R R = set of ratings R is a totally ordered set e.g., 0-5 stars, real number in [0,1] 1/30/2011 8 Jure Leskovec, Stanford C246: Mining Massive Datasets
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0.4 1 0.2 0.3 0.5 0.2 1 Avatar LOTR Matrix Pirates Alice Bob Carol David 1/30/2011 9 Jure Leskovec, Stanford C246: Mining Massive Datasets
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Gathering “known” ratings for matrix Extrapolate unknown ratings from known ratings Mainly interested in high unknown ratings Evaluating extrapolation methods 1/30/2011 10 Jure Leskovec, Stanford C246: Mining Massive Datasets
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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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  • Winter '09
  • Stanford University, Pearson product-moment correlation coefficient, User profile, Jure Leskovec, Cosine similarity, Mining Massive Datasets

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08-recsys - CS246 Mining Massive Datasets Jure Leskovec...

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