The media-oriented cross domain recommendation method

Abstract

With the rapid development of modern high-tech, such as big data and artificial intelligence, the demand for cross-media services is also greatly improved. Since different modes of cross-media data apply different dimensions and different attributes of the underlying features to present data, many tasks need to work collaboratively to handle multiform of information (including text, audio, video, image, etc.), so as to build cross-media analysis and reasoning. Through the cross-media, the method could express the same semantic information from their own side and could reflect the specific information more fully than a single media object and its specific modal. The same information is cross spread and integrated across different kinds of media objects. Only conducting fusion analysis to these multi-modal media, can ones fully and correctly understand the content information contained in the cross-media complex, which also adds difficulty in cross-media information recommendation process. At the same time, data sparsity, cold start and scalability issues of traditional recommendation system have long been unsolved, and as such it cannot adapt to the personalized service needs in cross-media applications. Focusing on the field of media information recommendation and taking the media data, user behavior data and project attribute information as the information source, this paper aims at researching the cross-domain recommendation algorithm. With the help of the label data, matrix decomposition, the author constructs a media-oriented cross-domain recommendation model in order to improve the recommendation accuracy and solve the data sparsity, cold start problems of media information recommendation technology, exploring a high-accurate media-oriented cross-domain recommendation method.