A Collaborative Filtering Algorithm with Phased Forecast

Conference Paper · July 2009with11 Reads
DOI: 10.1007/978-3-642-02962-2_88 · Source: DBLP
Conference: Rough Sets and Knowledge Technology, 4th International Conference, RSKT 2009, Gold Coast, Australia, July 14-16, 2009. Proceedings


    Collaborative filtering (CF) algorithms predict interests of an active user in order to deal with the overload of information.
    Usually, changes of her interests have been ignored in traditional algorithms, which take user’s interest as static data and
    product rating in different phase with same weight. So when users’ interests have changed as time goes on, unneeded items
    may be recommended. In order to solve above problem, we propose a new item-based collaborative filtering algorithm in this
    paper. In this algorithm, named PFCF, we firstly divide users’ rating history into several periods, then users’ interests
    distributing in these periods are analyzed by a phrased forecast method, which is used to find user’s different type interests.
    The proposed algorithm is strictly tested on the MovieLens data set. The experimental results show its good precision against
    other traditional item-based collaborative filtering algorithms.