By Rachsuda Jiamthapthaksin 10/09/2009 1 Edited by Christoph F. Eick.

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Presentation transcript:

By Rachsuda Jiamthapthaksin 10/09/ Edited by Christoph F. Eick

Recommender Systems (RSs)  Goal: To help users to find items that they likely appreciate (and buy/lease) from huge catalogues. 2

The recommendation problem  Let ○ C be the set of all users, and ○ S be the set of all possible items that can be recommended. ○ u be a utility function that measures the usefulness of item s to user c, u:C  S  R  For c  C, find s’  S that maximizes the user’s utility:  c  C, s ’ c = argmax s  S u(c,s)(1). 3

Netflix Recommender System Scenario 4  := unknown Remark: Typically, a lot of  symbols

Survey of the Netflix Contest  Netflix Prize competition offers a grand prize of US $1M for an algorithm that’s 10% more accurate than “Cinematch” Netflix uses to predict customers’ movie preferences.  The best score will win a $50K Progress Prize. 5

The Basic Structure of the Contest  Provide 100 million ratings that 480K anonymous customers had given to 17K movies.  Withhold 3M of the most recent ratings and ask the contestants to predict them.  Assess each contestant’s 3M predictions by comparing predictions with actual ratings.  Evaluation metric: the Root-Mean Squared Error 6

Netflix Dataset (1)  The data were collected between October, 1998 and December, 2005 and reflect the distribution of all ratings received during this period.  The ratings are on a scale from 1 to 5 (integral) stars.  The date of each rating and the title and year of release for each movie id are also provided. 7

Netflix Dataset (2)  training_set.tar (2 GB)  movie_titles.txt (575 KB)  qualifying.txt (51,224 KB)  probe.txt (10,530 KB)  rmse.pl (1 KB) 8