Agile and Personalized Information Production and Recommendations Through Signal Acquisition, with Shot Semantic Correspondence (Including Minimal Supervised Learning)

In this study, we propose a method to accomplish two tasks: transforming accumulated data from corporate production activities into knowledge, and recommending necessary information in a timely manner according to corporate activity policies and information users. This method extracts documents containing key phrases from large-scale language resources by leveraging stored text and numeric data. It then generates information expressing intent that encourages the information user’s decision making or some kind of action. Specifically, we propose a method that produces knowledge from past information and makes it reusable. By acquiring new information that serves as a signal for situational changes, the method supports interactive communication and recommends information that aids users in making personalized decisions within corporate activities. Specifically, while showing examples of the application of specialized knowledge domains in a specific customer segmentation within corporate activities, this method is centered on language and other numeric information generated within corporate economic activities that are recorded on databases. To express information that either represents the relationship between customers and companies or prompts decision-making or behavioral changes, domain-specific knowledge is built from various knowledge information processing tasks within economic activities. Then, through information acquisition that detects signals based on situational awareness, this method indicates that each text can be generalized into semantic representations as personalized information for users.