RecommendationSystems-1

RecommendationSystems-1 - CS345 Data Mining Recommendation...

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Unformatted text preview: CS345 Data Mining Recommendation Systems Anand Rajaraman, Jeffrey D. Ullman Recommendations Items Search Recommendations Products, web sites, blogs, news items, The Long Tail Source: Chris Anderson (2004) 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 Recommendation Types Editorial Simple aggregates Top 10, Most Popular, Recent Uploads Tailored to individual users Amazon, Netflix, Formal Model 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] Utility Matrix 0.4 1 0.2 0.3 0.5 0.2 1 King Kong King Kong LOTR LOTR Matrix Matrix National Treasure National Treasure Alice Alice Bob Bob Carol Carol David David Key Problems Gathering known ratings for matrix Extrapolate unknown ratings from known ratings Mainly interested in high unknown ratings Evaluating extrapolation methods Gathering Ratings Explicit Ask people to rate items Doesnt work well in practice people cant be bothered Implicit Learn ratings from user actions e.g., purchase implies high rating What about low ratings? Extrapolating Utilities Key problem: matrix U is sparse most people have not rated most items Three approaches Content-based Collaborative Hybrid Content-based recommendations Main idea: recommend items to customer C similar to previous items rated highly by C Movie recommendations recommend movies with same actor(s), director, genre, Websites, blogs, news recommend other sites with similar content Plan of action likes likes Item profiles...
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This document was uploaded on 03/04/2012.

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RecommendationSystems-1 - CS345 Data Mining Recommendation...

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