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R ECOMMENDER S YSTEMS : C ONTENT-BASED F ILTERING Krushal Desai Syed Raza Rizvi CSU Fullerton

December 5th , 2012

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I NTRODUCTION

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W HAT ARE WE DOING ? Learn how each user rates a certain genre of movie. Predict rating for unrated movies in similar genres.

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W HAT ARE WE ACTUALLY DOING ? (M ATH )

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T RAINING P HASE

Movie Lens data :1682 movies, 943 users, 10 genres (features). 80% of data = 1345 movies, 943 users, 10 features. Remaining 20% has zero ratings (i.e. they are not rated). Run Linear Regression algorithm.

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R ESULTS

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C AN WE DO BETTER ?

Use more features like: user type and release dates. Use more advanced algorithms: Logistic Regression. Use more evaluation methods: Cross Validation.

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Q UESTIONS

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Recommender System Final Presentation  

Beamer Presentation for Recommender System Project.

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