08-recsys

Cannot recommend an item that has not been previously

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Unformatted text preview: 6 Implement two or more different recommenders and combine predictions Perhaps using a linear model Add content-based methods to collaborative filtering item profiles for new item problem demographics to deal with new user problem 1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 27 movies 1 3 4 3 5 4 5 5 5 2 2 3 users 3 2 5 2 3 1 1 1 3 movies 1 3 4 3 5 4 5 5 5 ? ? 3 users 3 2 ? 2 3 1 1 ? ? Test Data Set (most recent ratings) Compare predictions with known ratings Root-mean-square error (RMSE) Precision at top 10: % of those in top10 Rating of top 10: Average rating assigned to top 10 Rank Correlation: Spearman’s, rs, between system’s and user’s complete rankings. Another approach: 0/1 model Coverage Number of items/users for which system can make predictions Precision Accuracy of predictions Receiver operating characteristic (ROC) Tradeoff curve between false positives and false negatives 1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 30 Narrow focus on accuracy sometimes misses the point Prediction Diversity Prediction Context Order of predictions In practice, we care only to predict high ratings: RMSE might penalize a method that does well for high ratings and badly for others 1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 31 Common problem that comes up in many settings Given a large number N of vectors in some high-dimensional space (M dimensions), find pairs of vectors that have high similarity e.g., user profiles, item profiles We already know how to do this! Near-neighbor search in high dimensions (LSH) Dimensionality reduction 1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 32 Exoticness / Price Overview of Coffee Varieties B2 B1 I2 I1C6 L5 Exotic S5 C1 S1 S2 S7 S6 C7 R4 S3 R6 R3 R2 C2 C4 a1 L4C3S4 FR TE F9 R5 R8 Popular Roasts and Blends Flavored F8 F3 F2 F1 F0 F6 F5 F4 Com plexity of Flavor The bubbles above represent products sized by sales volume. Products close to each other are recommended to each other. 1/30/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 33...
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This document was uploaded on 02/26/2014 for the course CS 246 at Stanford.

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