Study of Short Video Popularity Prediction Based on Network Representation Learning
Predicting the popularity of short videos not only helps short-video platforms with efficient information man-agement but also plays an important role in monitoring public opinion.Unlike existing studies that focus only on multi-modal content features of short videos,to construct a popularity prediction model,we propose a popularity prediction mod-el based on network representation learning,fusing content and network structural features.First,based on the dataset crawled in Douyin,a heterogeneous information network consisting of nodes was constructed,including short videos,pub-lishers,commenters,and edges.After mapping into two different homogeneous networks,namely,short-video and publish-er networks,node2vec was selected to represent the network structure in the embedding space as a network modality.Sec-ond,the multimodal content features of short videos were extracted and fused using low-rank multiview embedding learn-ing.Finally,a multilayer perceptron machine regression model was proposed for short-video popularity prediction.Com-parisons and ablation experiments were further conducted.The results show that fusing network structure features can re-duce the error of short-video popularity prediction.The degree of influence of the various modalities on short-video popu-larity prediction consisted of the textual,network,social,acoustic,and visual modalities,in decreasing order.Our method,which combines short-video content and network structure features,provides new ideas for short-video popularity predic-tion based on feature engineering.
short videonetwork representation learningpopularity predictionmultilayer perceptron
NETL
NSTL
万方数据
