Research On The Accurate Information Recommendation Method Based On Scenario Perception

The advent of the Web 2.0 model has led to tremendous changes in the way information is exchanged over the Internet.On one hand,vast amounts of information enrich the user's travel experience and increase user satisfaction.On the other hand,due to the wide variety and quantity of information on the Internet Exponential growth,the user is difficult to quickly find information related to their own massive data,triggering the "information overload","information obsession" and other issues,Personalized recommendation system through the analysis of the user submits the information they are interested in and explore the potential of the user browsing process,targeted to recommend information to users,effectively solve the current problems due to the rapid development of the Internet.However,tourism information is not only a large number,but also has the characteristics of diversity,dynamic and real-time.Collaborative filtering algorithm is the core of personalized recommendation system,but it also faces the problem of sparseness and cold start-up,and to a certain extent neglects In this case,this paper proposes a context-aware collaborative filtering algorithm that combines context-aware and collaborative filtering algorithms through late-filtering scenarios to improve the effectiveness of the proposed algorithm Then,from the perspective of the user,a user interest model based on context awareness is established,which improves the accuracy and efficiency of the personalized information recommendation service and enhances user satisfaction and travel experience.The main research contents are as follows:(1)Through the research on personalized recommendation system and situational a wareness both at home and abroad,it is found that the behavior of users is inextricably linked to their situation.Firstly,the definition of dynamic and static scenarios is given.Then,the tourism scenarios are classified and the service framework of context a wareness is constructed.The service process of context awareness is well described.Finally,three methods of scenario awareness and personalized recommendation are selected.The appropriate recommendation mode is combined with the collaborative filtering algorithm.(2)Because the collaborative filtering algorithm only considers the scoring information of users and projects and causes the data sparseness,and the existing algorithms lack the analysis of the scene information,the user context information is introduced and a collaborative filtering algorithm based on context awareness is proposed.In the MovieLens data The set uses the Matlab method to compare the collaborative filtering algorithm and the collaborative filtering algorithm based on the situation awareness.Experiments show that the improved algorithm has higher superiority in the accuracy of the neighboring value and the overall prediction.(3)User behavior changes from time to time,so this paper constructs a user interest model based on situational awareness.This model adds user context awareness based on the existing user interest model,extracts the user's scenario interest items,and realizes the user's Dynamic changes,thus restoring the user's real interest,the use of MapReduce data parallel recommendations to provide users with fast,real-time,accurate service.(4)Design a tourism intelligence recommendation system based on situational awareness and realize the accurate recommendation of tourist information.The system through the tourism intelligence recommendation system,the overall framework of the data base,the design of system functions related to deal with the relevant data,and ultimately achieve the intelligent function of tourism information push precision.