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Personalized service is becoming a hot topic in information processing field. Meanwhile, mobile network operators pay much more attention to information service than they used to do. It is necessary to provide pertinent service to different customers if different users have different demands. To meet the need of personalized service system and aim to implementing personalized information recommendation, a new model on mobile networks is purposed at the beginning of this paper. Then the paper focuses on research on the relationship between users' behavior of using the system and their preference and discussing the interests reflected by those users' log files. The paper gives an algorithm of mining user's true preference on mobile network. Then the paper introduces a personalized information recommendation engine to implement recommending valuable information items to users. In this paper, some background of information service system on mobile networks is introduced firstly. Then a new model of this personalized service system is described. The association between user's data and their hidden preference as well as a concerned algorithm is discussed. Then, two important methods to implement personalized service, clustering and mining association rules, are depicted. An experiment to check the model and these algorithms is introduced and analyzed at the end of the paper.