Study and Analysis of Clustering Based Scalable Recommender Systems for E-Commerce
نویسندگان
چکیده
Collaborative filtering based recommender systems help online users in choosing the right products based on the purchase history of the user and his most similar users. Scalability is one of the major issues in designing effective recommender system. In this paper, we have studied different ways of increasing scalability by applying clustering algorithms on three types collaborative filtering algorithms-user based, item based and slope one. Finally we have analyzed the relationship between scalability and accuracy for different number of clusters and neighborhoods.
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