Research on Information Popularity Prediction Based on Multi-Dimensional Feature Fusion:A Case Study of Campus Information Platforms
[Research purpose]In the face of information overload on campus information platforms,how users can discover and obtain hotspot information from a vast amount of data has become an urgent problem.[Research method]To more accurately capture and pre-dict the real popularity of information,this study proposes an information popularity prediction model MIHP based on multi-dimensional feature fusion.This model not only considers the analysis of text content but also incorporates the extraction and analysis of non-text fea-tures into the algorithm process.Through this multi-dimensional feature fusion,the model can more comprehensively assess the attractive-ness and potential for dissemination of information,thereby more accurately reflecting the real popularity of the information.[Research conclusion]Experimental results conducted on the campus information platform dataset show that the MIHP model outperforms other base-line models,providing a new solution for information popularity prediction.
information popularitymulti-dimensional feature fusionattention mechanismcampus information platformpopularity pre-diction model
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