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摘要
Due to the large amount of live streaming data, diverse data modes, strong real-time variability,and the hot spot effect of live streaming data,it poses a higher challenge to the accurate recommendation of live streaming resources. In order to improve the effectiveness of live streaming recommendation,a live streaming recommendation algorithm based on multi-modal neural network is proposed to provide technical guarantee for various large media platforms. Firstly, the scoring criterion of live streaming recommendation is established through the user’s viewing time and scoring weight matrix. Then,on the basis of text data samples,multi-modal features such as video,pictures and audio are selected as input data samples. The multi-modal neural network is used to establish the recommendation algorithm of users and live streaming resources,and the regular loss function of live streaming recommendation is constructed. Taking the minimum value of loss function as the optimization object,the optimal algorithm is obtained by iteration. Finally,the multi-modal features are input to obtain the live TOP-K recommendation sequence, and the performance of the recommendation sequence is evaluated. The experimental results show that the proposed live streaming recommendation algorithm achieves high recommendation performance in five different media platform data by reasonably setting the layers of neural network,which provides effective strategic support for accurate resource recommendation of live streaming platforms. ? 2023 Nanjing University of Science and Technology.
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