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In order to enrich the recommendation of e-commerce functionality and optimize the user experience of online shopping, a new style matching model-based recommend method of e-commerce was proposed. The Content-Based Image Retrieval(CBIR) technology was used to extract the image feature of color, texture and shape of product, calculated the commodity cluster, which have the similar feature of image content, then was applied to the style matching model, recommend to the user in accordance with the user's cognitive style. The style matching mode-based recommendation engine was developed, and integrated into the e-commerce recommend system to help users complete the cognitive of style and guide the online shopping. Finally, a shoe buying recommend system was developed to verify the theory.

In this paper, a novel low-complexity narrow-band interference (NBI) suppression scheme is proposed which is based on singular value decomposition (SVD) and segment technique. Simulation results show that the presented scheme can eliminate NBI effectively. And its computational complexity is lower than conventional methods.