Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the intro...Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.展开更多
In new environments of trading, customer's trust is vital for the extended progress and development of electronic commerce. This paper proposes that in addition to known factors of electronic commerce B2C websites...In new environments of trading, customer's trust is vital for the extended progress and development of electronic commerce. This paper proposes that in addition to known factors of electronic commerce B2C websites such a design of websites, security of websites and familiarity of website influence customers trust in online transactions. This paper presents an application of expert system on trust in electronic commerce. Based on experts’ judgment, a frame of work was proposed. The proposed model applies ANFIS and Mamdani inference fuzzy system to get the desired results and then results of two methods were compared. Two questionnaires were used in this study. The first questionnaire was developed for e-commerce experts, and the second one was designed for the customers of electronic websites. Based on AHP method, Expert Choice software was used to determine the priority of factors in the first questionnaire, and MATLAB and Excel were used for developing the fuzzy rules. Finally, the fuzzy logical kit was used to analyze the generated factors in the model. Our study findings show that trust in EC transactions is strongly mediated by perceived security.展开更多
基金National College Students’Training Programs of Innovation and Entrepreneurship,Grant/Award Number:S202210022060the CACMS Innovation Fund,Grant/Award Number:CI2021A00512the National Nature Science Foundation of China under Grant,Grant/Award Number:62206021。
文摘Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.
文摘In new environments of trading, customer's trust is vital for the extended progress and development of electronic commerce. This paper proposes that in addition to known factors of electronic commerce B2C websites such a design of websites, security of websites and familiarity of website influence customers trust in online transactions. This paper presents an application of expert system on trust in electronic commerce. Based on experts’ judgment, a frame of work was proposed. The proposed model applies ANFIS and Mamdani inference fuzzy system to get the desired results and then results of two methods were compared. Two questionnaires were used in this study. The first questionnaire was developed for e-commerce experts, and the second one was designed for the customers of electronic websites. Based on AHP method, Expert Choice software was used to determine the priority of factors in the first questionnaire, and MATLAB and Excel were used for developing the fuzzy rules. Finally, the fuzzy logical kit was used to analyze the generated factors in the model. Our study findings show that trust in EC transactions is strongly mediated by perceived security.
