Collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. we propose a new collaborative filtering algorithms, ratio-based collaborative filtering algorithms, by calculating the ratio between the ratings of one item and another for users who rated both to predict the ratings. Ratio-based collaborative filtering algorithms are easy to implement, and have reasonably accurate, by factoring in the weighted average methods and the preference parameter, we achieve results competitive with traditional memory-based algorithms over the Movielens data sets. The result is sufficient to support our claim.


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    Title :

    Ratio-based collaborative filtering algorithms


    Contributors:
    Yaqiu Liu, (author) / Zhendi Wang, (author) / Man Li, (author)


    Publication date :

    2008-12-01


    Size :

    477602 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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