Personalized Recommendations Of The Campus Mobile Internet Value-added Services

With the development of the various wireless broadband technology, and thepopularity of the various mobile devices, users can access and use the Internetservices when they moves, they also can enjoy the convenience brought by theInternet Value-added services anytime、anywhere. At this time, all kinds of mobileInternet value-added services which can meet consumer demand for high-levelinformation timely in the market, and gradually towards diversification. In so manyValue-added services, how to let users find the services which they are interestedquickly and accurately? Obviously the traditional way has been unable to solve thisproblem very well.In order to solve this issue, in the realm of internet, customized recommendationsystem, particularly in the direction of E-commerce, has been introduced to themarket and realized certain achievement. In recent years, the application ofcustomized recommendation system started to catch users’ eyes, and domesticscholars have designed and developed such customized recommendation systems thatcan recommend customers with catered news, mobile ringtones and etc. However,though customers in the school contribute as an important and special users group, thetraditional customized recommendation system has not been well applied on campus,which needs to be improved.This paper briefly introduces the theoretical knowledge of mobile internet,value-added services and customized recommendation systems, and then researcheson the customized recommendation systems with respect to users’ Collaborativefiltering. Based on the existing customized recommendation system, this paper willintroduce the weight of business impacts in the calculation of similarity among users.At the meanwhile, the hierarchy analysis will be applied in this paper in order toconvert implicit empirical judgments into explicit numerical weights, minimizing theimpacts brought by the compulsory services. Additionally, the paper also considers thedistinctiveness of student users group and therefore adds economic factor which canfurther develop the methodology of collaborative filtering。It designs and realizes acustomized recommendation system of value-added services which is specifically catered for campus users, aiming to improve recommendation accuracy, facilitateusers experience and bring operators more profits.At the end of this paper, by using questionnaire statistics, we conduct twocontrasting empirical researches respectively on the traditional calculationmethodology of customized recommendation system and the improved one whichintegrates the economic factor and the result shows that the improved methodologycan effectively increase the recommendation accuracy of value-added services.