Design And Implementation Of Point-of-Interest Recommendation System Based On Deep Knowledge Learning

With the development of the times,information technology has evolved from a new term to the background of the times,and the Internet is constantly changing people's lives.Especially,location-based social network(LBSN)is widely used in various fields of people's life.As a very important research content in LBSN,personalized point-of-interest(POI)recommendation is a hot topic in the field of scientific research and production.In the current information explosion era,how to recommend the point-of-interest for users effectively and efficiently is a key problem.In order to solve the pain point problem of point-of-interest recommendation,this paper designs and implements a point-of-interest recommendation system based on deep knowledge learning.The main work includes:(1)In this paper,knowledge graph embedding is applied to the recommendation system,and a low dimensional vector representation is obtained for each entity and each relationship in the knowledge graph by using the knowledge representation model based on translation.(2)The improved time long short-term memory(TLSTM)neural network model based on long short-term memory(LSTM)and attention mechanism is used to predict user preferences.(3)Design and development of the recommendation system of point-of-interst is completed.The system adopts a hierarchical architecture mode,from top to bottom is the interaction layer,online layer,offline layer.The interaction layer is the final visualization result of the system,which defines the interaction between users and merchants through the web end,and realizes the function of serving the three roles of point-of-interest merchants,platform users and system administrators.The online layer includes model base and algorithm base,which calculates the data from the interaction layer and returns the recommendation results.The offline layer includes modeling part and data storage part,corresponding to model training and data storage function respectively.The system has passed the function tests and performance tests,and the whole system meets the expected standard.