Based on the introduction of basic collaborative filtering algorithm, six kinds of techniques which are used to ameliorate the scalability problem are generalized, including clustering, probabilistic approach, dimensionality reduction, item-based, dataset reduction and linear model. The collaborative filtering algorithms with aforementioned techniques are commented emphatically, and their ideas are summarized in two points: reducing the neighborhood search space under the precondition of unaffected recommendation quality; periodically running user similarity measuring and neighborhood research offline to reduce the recommendation computation online. Two future research directions on the scalability problem in collaborative filtering are discussed finally, namely the collaborative filtering algorithm based on distributed structure, and the neighborhood search based on formal concept analysis.
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