A weighted sifting method to improve the effectiveness of collaborative filtering

In this paper, we improve the accuracy of the conventional collaborative filtering algorithm by proposing a weighted sifting method. The weighted sifting method preprocesses the given customer data to generate an adjusted customer data which we believe contains less noise than the original one, and thus effectively discriminates the preference weights of items for each customer. We present two alternative calculation methods for weight adjustment, and our experimental evaluation shows that both calculation methods result in better accuracy than traditional collaborative filtering.