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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    Titel :

    Ratio-based collaborative filtering algorithms


    Beteiligte:
    Yaqiu Liu, (Autor:in) / Zhendi Wang, (Autor:in) / Man Li, (Autor:in)


    Erscheinungsdatum :

    01.12.2008


    Format / Umfang :

    477602 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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