Research And Application Of An Intelligent Recommemder System

With the popularity of Internet technology applications, the exponential growth trend in the number of flooding the network resources. The flood of information presented to the user at the same time, the "information explosion" and "information overload" phenomenon. The personalized recommendation system is an information service technology to alleviate this problem, it is based on user history and behavior information to build user interest model recommended by the information that may be of interest to the user through the model. Recommendation system on the one hand in the massive amounts of data by predicting user preference of the project to provide users with information filtering, application of knowledge discovery technology to generate personalized recommendation to help users find the information they need; other hand auxiliary enterprises achieve personalized marketing purpose, and thus increase sales, create more profits for the enterprise.Recommendation system with good development and application prospects has become an important research direction in Web intelligence technology, widespread concern by many researchers. In the past two decades, the personalized recommendation technology has been rapid development. With the advent of the era of big data, especially in the recommended system widely used in e-commerce, advertising push, there are still many problems to be solved in the study of the rapid growth of the music and movie recommendation data, personalized recommendation system. In this thesis, the key technologies for intelligent recommendation system user modeling and recommendation algorithm exploration and research. This thesis mainly personalized recommendation technology used in the recommendation system for enterprise decision-making. The main contents of this paper are as follows:(1) Similarity calculation does not consider some of the problems brought about by the user location context information based collaborative filtering recommendation system; this paper presents the design of a collaborative filtering algorithm based on location context information. The first according to the user’s location information of the system the calculation of the distance attenuation and the degree of similarity, and then based on the user interest in behavior between to build user preference relationship network; obtained through a combination of both, the degree of similarity between the users; Finally recommended for users.(2) The business model is the intelligent recommendation foundation and a key part of a direct impact on the pros and cons of the recommendation service. Attributes drawn from business decisions to influence decision-making, and to extract the information structure, the attribute organization for business and influence decision-making based on the attribute characteristics relational model to calculate the similarity matrix model, then the model is mapped to fit. On the basis of the proposed business model, this paper designed to achieve the recommended intelligent recommendation system, a decision-making and in the application of "national eugenics before pregnancy health check information system".