Collaborative filtering has been widely used in the recommendation system

but the traditional algorithm has some limitations

such as inability to adapt to the sparsity of user-item rating matrix data sets well

failure to consider the classification of item

users-scores

interest change over time and other factors when calculating the similarity of the items. Regarding these limitations

it proposed a big data set hybrid dynamic collaborative filtering algorithm

based on the traditional collaborative filtering algorithm. When calculating the similarity of items

time decay functions were introduced in the algorithm

which considered both the similarity of items

scores and items classified. The weights of project integrated similarity could be adjusted automatically. In the algorithm

some improvements have also been made in similarity computing and the selection of the neighboring items. To verify the effectiveness of the algorithm

experiments were done on movielens data sets. Experimental results show that the algorithm is better than the traditional recommendation algorithms.