Combing Probabilistic Matrix Factorization withDeep Learning Networks in Recommender System

  • 邱 怡瑄

Student thesis: Doctoral Thesis


Before the recommender system generating recommendation results we need to filter out useful information from the big data but the information may be explicitor implicit The results of the traditional Matrix Factorization usually consider the accuracy of the first few results Therefore considering the global covariance is often inaccurate Studies have shown that the probabilistic matrix factorization can produce good recommendations but the disadvantage is that it only considered the product rating in the model And in other studies the recommended results using Probabilistic Matrix Factorization are not well explained Exampleblei et al (2003) proposed the Latent Dirichlet Allocation to generate the topic model which always ignores the context of each word Therefore Wang and Blei (2011) proposed the Collaborative Topic Modeling for recommending scientific articles which combining the Probabilistic Matrix Factorization model and the Latent Dirichlet Allocation model to generate the user recommendation The effect is significantly better than the original Matrix Factorization method and has more accurate predictions To clarify the user’s preferences the methods related to sentimental text analysis can perform well and also can extract the product feature and sentimental text from the comment The natural language process has been extensively researched since the millennium The deep learning network has excellent performance in text mining and topic analysis Most of the text sentiment analysis methods can’t give the order of the best or bad in the feature of the user’s favorite products Therefore this thesis combines deep learning sentiment analysis and Probabilistic Matrix Factorization to find out the user’s true preferences
Date of Award2019
Original languageEnglish
SupervisorRen-Shiou Liu (Supervisor)

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