Personalized collaborative filtering based on improved slope one alogarithm

Predicting products a customer would like on the basis of other customers ratings for these products has become a well-known approach adopted by many personalized recommendation systems on the Internet. With the development of electronic commerce, the number of customers and products grows rapidly, resulted in the sparsely of the rating dataset. Poor quality is one major challenge in collaborative filtering recommender systems. To solve this problem, slope one algorithm has a good performance. But there is also some places that not make sense. Slope one algorithm assumes that all users are at the same level, and it has neglected the individual differences. So, we propose an improved slope one algorithm, which will consider the weights of the user. Finally, we experimentally evaluate our approach and compare it to the original Slope One. The experiment shows that our method provides better recommendation results than it.