This paper comparatively analyzes the existing evaluation index of websites, and puts forward the evaluation index and method about the support of a website to enterprise's e-commerce. Through researching on 56 su...This paper comparatively analyzes the existing evaluation index of websites, and puts forward the evaluation index and method about the support of a website to enterprise's e-commerce. Through researching on 56 super enterprises of information industry in Sichuan province, throughout China and the world, analyzing and comparatively studying the support ability of a website to an enterprise's e-commerce, this paper brings forward using five levels to categorize the support ability of a website to enterprise's e-commerce. In the end, the flaw of enterprise's e-commerce practice in Sichuan province and corresponding countermeasure will be illustrated.展开更多
Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external ...Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.展开更多
文摘This paper comparatively analyzes the existing evaluation index of websites, and puts forward the evaluation index and method about the support of a website to enterprise's e-commerce. Through researching on 56 super enterprises of information industry in Sichuan province, throughout China and the world, analyzing and comparatively studying the support ability of a website to an enterprise's e-commerce, this paper brings forward using five levels to categorize the support ability of a website to enterprise's e-commerce. In the end, the flaw of enterprise's e-commerce practice in Sichuan province and corresponding countermeasure will be illustrated.
文摘Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.
