Concerning data sparseness, low accuracy and poor real-time performance of traditional collaborative filtering recommendation algorithm in e-commerce system under the background of big data, a new collaborative filtering recommendation algorithm based on improved clustering and matrix decomposition was proposed. Firstly, the dimensionality reduction and data filling of the original data were reliazed by matrix decomposition. Then the time decay function was introduced to deal with user score. The attribute vector of a project was used to characterize the project and the interest vector of user was used to characterize the user, then the projects and users were clustered by k-means clustering algorithm. By using the improved similarity measure method, the nearest neighbors and the project recommendation candidate set in the cluster were searched, thus the recommendation was made. Experimental results show that the proposed algorithm can not only solve the problem of sparse data and cold start caused by new projects, but also can reflect the change of user's interest in multi-dimension, and the accuracy of recommendation algorithm is obviously improved.

Due to the spatial distribution expression power of the ring areas, this paper proposed a new image retrieval algorithm based on color feature and texture feature of interest points, introducing the ring area into the Gabor wavelet texture features. At first, this algorithm used adaptive smoothing filter to eliminate noises and detected interest points by the Speeded Up Robust Feature (SURF) operator. Then, it calculated the color histogram and texture feature of interest points in the ring area, as images combined features. Finally, it output similar images according to the similarity of the combined features. The experimental results show that this algorithm improves the average retrieval accuracy by more than 7%.