Methods for products recommendation using emotional information

Ju, Hongli; Jin, Guangzhong; Namkamori, Yoshiteru

doi:10.1109/icsmc.2010.5641865

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Uncertainty has become an ubiquitous kind of knowledge in various applications, such as medical diagnostics [22,26], social raring analysis and user behavior modeling [19], product recommendation [15], web search ranking [21], and so on. As the well adopted framework of uncertainty representation and inferences, BN represents the dependent and independent relationships among a set of variables based on the graph and probability theories.…”

Section: Introductionmentioning

confidence: 99%

Structure Learning of Bayesian Network with Latent Variables by Weight-Induced Refinement

Yue

et al. 2014

Proceedings of the 5th International Workshop on Web-Scale Knowledge Representation Retrieval &Amp; Reasoning

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Bayesian network (BN) with latent variables (LVs) provides a concise and straightforward framework for representing and inferring uncertain knowledge with unobservable variables or with regard to missing data. To learn the BN with LVs consistently with the realistic situations, we propose the information theory based concept of existence weight and incorporate it into the clique-based learning method. In line with the challenges when learning BN with LVs, we focus on determining the number of LVs, and determining the relationships between LVs and the observed variables. First, we define the existence weight and propose the algorithms for finding the ε-cliques from the BN without LVs learned from data. Then, we introduce the LV to each ε-clique and adjust the BN structure with LVs. Further, we adjust the value of parameter ε to determine the number of LVs. Experimental results show the feasibility of our method.

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Section: Introductionmentioning

confidence: 99%

Structure Learning of Bayesian Network with Latent Variables by Weight-Induced Refinement

Yue

et al. 2014

Proceedings of the 5th International Workshop on Web-Scale Knowledge Representation Retrieval &Amp; Reasoning

View full text Add to dashboard Cite

show abstract

Methods for products recommendation using emotional information

Cited by 1 publication

References 26 publications

Structure Learning of Bayesian Network with Latent Variables by Weight-Induced Refinement

Structure Learning of Bayesian Network with Latent Variables by Weight-Induced Refinement

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