Abstract:As a key solution to the problem of information overload, the recommender system can filter a large deal of information according to user’s preference and provide personalized recommendations for the user. However, traditional collaborative filtering models with excellent performance haven’t made full use of the contextual information in the process of recommendation, which to some extent confronts the system with the performance bottleneck. In order to improve the system performance further, this paper starts with the contextual information on ratings, and proposes a collaborative filtering model fusing singularity and diffusion process (CFSDP) by taking advantage of ratings’ singularities obtained from the classified statistics of ratings and referring to the similarity model of multi-channel diffusion which regards recommender system as a user-item bipartite network. To demonstrate the superiority of the proposed model, the study provides comparative experimental results based on the MovieLens, NetFlix and Jester data sets. Finally, the results show that the model not only has better extensibility, but also can observably improve the prediction and recommendation quality of system with a reasonable time cost.

Abstract:It is very important to detect singularities (core and delta) accurately and reliably for classification and matching of fingerprints. In this paper, a method for singularity detection in fingerprint images is presented to improve accuracy of the position and reliability of the singularity. Firstly, the singularities are detected based on block images through shifting position of the whole image time after time at the same block size and the concentrative region of singularities detected under different positions is got and the centroid of the region is computed to gain the accurate position of singularities. Then, the reliability of singularities detected above is determined with multilevel block sizes. In this method, the characteristics of the relative concentration of the position of singularities detected through image shift and of the corresponding relationship of the singularities detected with multilevel block sizes are used and the singularities are detected accurately and reliably. Experimental results show that the method performs well and it is robust to poor quality images.