A Generation Model of Mixed Difficult and Negative Samples in Recommendation System
Negative samples have a significant impact on collaborative filtering recommendation tasks,and high-quality negative samples can help models accurately describe user profiles.A hybrid dynamic negative sampling model is proposed based on the idea of difficult negative samples to address the existing problems of false negative samples and high computational complexity.Firstly,the range and sequence of nega-tive samples for each user are determined through dynamic negative sampling methods and service recommendation models;Then quickly sam-ple a large number of difficult and negative sample candidates for each user;Next,using a hybrid approach,assemble the sampled negative sample set into a difficult negative sample to expand the perceptual domain and incorporate more information;Finally,an attention mecha-nism is introduced to guide the fusion of negative samples,thereby improving system stability.Comparative experiments with baseline models on publicly available datasets in Alibaba,Yelp2018,and Amazon have shown that the proposed model outperforms existing baseline models under multiple evaluation metrics,demonstrating the effectiveness of the model.
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