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Research Challenges in Recommender Systems / Survey of the Netflix Contest

By Rachsuda Jiamthapthaksin 10/09/2009. Research Challenges in Recommender Systems / Survey of the Netflix Contest. 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.

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Research Challenges in Recommender Systems / Survey of the Netflix Contest

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  1. By Rachsuda Jiamthapthaksin 10/09/2009 Research Challenges in Recommender Systems / Survey of the Netflix Contest Edited by Christoph F. Eick

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

  3. 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 = argmaxsSu(c,s) (1).

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

  5. 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.

  6. 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

  7. 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.

  8. 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)

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