User Content Preference Completion Based On Telecommunication Data

User preference profile is generally significant to marketing strategy decisions and user experience improvement for mobile telecom operators,in which user network content preference profile is an important part of preference profile.Taking that operators can't completely record user preference information by deep package inspection(DPI)technique,it is an important research direction to establish a complete and reliable user content preference profile by using a variety of telecommunication data.This paper aims to complete the users' content preference from the perspective of preference taxonomy and user feature information in the telecommunication data set.The paper designs and implements the parallel user content preference profile complementation model.1.Model Design and Implementation:Since the user's content preference can't be completely recorded,this paper combines the preference complementation problem with the traditional Top-N recommendation problem and using the latent factor model as basic model,solving preference complementation problem by using a variety of telecommunication data:(1)According to the preference information in data set,this paper defines the relationship between preferences from the perspective of hierarchical taxonomy relations,and defines the relationship between preferences from the aspects of type and strength to expand the latent factor model.(2)According to the user information in the data set,this paper the user features by mining and quantifying,and then select the high quality feature set in the experiment to expend the latent factor model by user features.(3)In the meantime,aiming at the problem that the traditional single machine algorithm can't accomplish the task of large amount of data quickly,the paper completes the parallel implementation based on the distributed system,which can accomplish the preference complementation problem of large amount of user in a short time.2.Experimental evaluation and exploration research:Through the comparison of model experiments and high-quality feature recognition of telecom multi-type datasets provided by operators for four months,it is found that by fully mining telecom data,the model performance has been greatly improved by our model compared to the basic model.In addition,based on the experimental results,this paper explores the relationship between preferences and the distribution of high quality features.3.System Design:On this basis,this article designs and implements the mobile user content preference profile system.