Abstract:

The traditional collaborative filtering recommendation method suffers from sparse datasets, cold starts, and efficiency problems. Furthermore, recommend accuracy decreases with an increase in the amount of data. Therefore, we improved the traditional collaborative filtering recommendation method by increasing the same rating between users when calculating their similarity and running it on a cluster. Because of the above actions, the collaborative filtering recommendation method obtains a better accuracy. Through experiments, we saw that the method we proposed has higher accuracy and efficiency compared to traditional collaborative filtering recommendation methods.