Research On Collaborative Filtering Algorithm And Its Application In Base Station Location System

In recent years,with the rapid development of mobile Internet,the location of base stations in the field of communications is increasingly important.With the implementation of the reform of "separation of internet and business" in China and the development of site selection of base stations,the related information of base stations and site selection agents has also been increased in large quantities.In the new situation,how to recommend a suitable site selection officer for a base station rather than a random selection of a large number of site selection agents has drawn more and more attention from industry insiders and has been extensively studied.Collaborative filtering algorithm is a recommended algorithm,it can not only be used to help users understand business activities,but also can help us recommend the most suitable satelists from a large number of selected members,thus reducing labor costs while increasing the time effectiveness.First of all,this thesis analyzes the background and significance of the thesis topics,and studies the role played by base station location system and recommendation system in site selection.Secondly,we study the recommended algorithms commonly used in the recommendation system and the advantages of the collaborative filtering algorithm compared with other algorithms.Thirdly,the thesis analyzes the system model of collaborative filtering algorithm and its shortcomings in actual use.Based on the deficiency of the system model of collaborative filtering algorithm,a loss function based collaborative filtering algorithm is proposed.The convex optimization theory is used to solve its parameters,and the parameters of the loss function based collaborative filtering algorithm are learned,so that the traditional statistical-based algorithm becomes an algorithm capable of continuous self-learning.Through simulation experiments,it has been proved that its performance and industrial availability have been greatly improved to verify the improvement of the rationality and effectiveness.Finally,based on the research results of the first three chapters on collaborative filtering algorithms,this thesis applies the collaborative filtering algorithm introduced in the loss function to the problem of site selector recommendation under the scenario of base station site selection,and designs and implements a recommendation function.Base station site selection system.The data in the site selection scenario of the base station proves the effectiveness of the improved collaborative filtering algorithm and the rationality of the application of the collaborative filtering algorithm that introduces the loss function to the candidate recommendation problem.The research and application of collaborative filtering algorithm,the introduction of the recommended module based on the loss function based collaborative filtering algorithm,greatly enriches the function of the site selection system of the base station and makes it an intelligent system.