The multimedia recommendation algorithm based on probability graphical model
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The multimedia recommendation algorithm based on probability graphical model Chen Li 1 Wei Wu 2
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& Yu Li & Chunhua Wang & Shifeng Dong & Haofei Gao & Qian Zhao &
Received: 31 March 2020 / Revised: 17 August 2020 / Accepted: 19 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Multimedia recommendation . Probability graphical model . Undirected graphical model . Collaborative filtering . Bayesians Network
1 Introduction As a result of the rapid progress of Internet, personalized multimedia recommendation system has been applied in various industries and has been playing a significant role. The e-commerce
* Chen Li [email protected]; [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
giants, like the pioneering Amazon, Taobao, JDcom and Suning, they all enjoyed powerful personalized recommendation system. They know what customers put in the shopping cart and what they buy, and recommend the personalized items for them, thus boost their sales. Hence, personalized recommendation system has been used widely in different sectors and is of great value to academic research. The multimedia field contains various form of information, including documents, videos, sounds and others, which are all conveyed to user by digital data. These kinds of resources are semi-structured data, user choose proper multimedia resources to browse or share through mobile devices and media outlets. Many multimedia recommendation systems in this fields
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