Abstract
In the multimedia big data, the demand for personalized multimedia recommendation algorithm is increasing to ease the multimedia information overload. The multimedia recommendation system has been applied in various industries and has been playing a significant role. With the development of multimedia big data, developing multimedia recommendation algorithms can effectively be used in multimedia data. However, a large number of prevailing recommendation systems cannot meet the multimedia recommendation requirements, since they ignore the user-item interactions with multimedia content. This essay realizes the multimedia recommendation based on probability graphical model, to deal with the cold start and data sparsity involved in collaborative filtering recommendation, proposing that add the user tag to user-item model. The essay optimizes the multimedia recommendation algorithm based on undirected graphical model and tests it with singular value decomposition, clustering and Naïve Bayes separately. The essay also builds the checklist recommendation model and experiments extensively for comparison with the conditional multimedia recommendation algorithm, by using PersonalRank algorithm based on random-walk to work out the weight coefficient of the user tag. At the same time, the essay enhances the probability-graph multimedia recommendation algorithm by dimensionality reduction and clustering, with the result of noticeably improved precision and recall.
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The work is supported by the Fundamental Research Funds for the Central Universities and National Key Research and Development Program of China (2018YFC0832000). We thank the reviewers and editor for their helpful comments.
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Li, C., Li, Y., Wang, C. et al. The multimedia recommendation algorithm based on probability graphical model. Multimed Tools Appl 81, 19035–19050 (2022). https://doi.org/10.1007/s11042-020-10129-8
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DOI: https://doi.org/10.1007/s11042-020-10129-8