Research On Academic Resource Recommendation Service Based On User Profile

With the advent of the Big Data Era and information overload,it's very difficult for academic users to obtain the information they want quickly and accurately faced with massive academic resources.Therefore,aiming at the optimization of academic resource recommendation service,in this thesis,it constructs a set of multi-dimensional academic user profile model.At the same time,it proposes an academic resource recommendation algorithm based on user profile.Firstly,to review and sort out literatures related to academic resource recommendation services and user profiles.Secondly,in order to obtain the attribute tags of multi-dimensional user profiles,a set of questionnaires were designed to collect the real information of academic users,generating the corresponding academic user profile model.Then,the collected data are processed by certain rules,and quantitative modeling is carried out on the basis of the data by mathematical means.Finally,the completed academic user profile model and collaborative filtering algorithm are constructed to provide personalized academic resource recommendation service for academic users.Through the verification and analysis of simulation experiments,the academic resource recommendation algorithm based on user profile proposed in this paper plays a great role in expanding users' interest fields and discovering new cross-field and interdisciplinary interests.The main innovative achievements of this thesis are reflected in the following three points:1)when academic users are facing vast amounts of resources,unable to obtain accurate and personalized recommendation service,it describes and characterizes academic users in an all-round and multi-level way,accurately reflects users' characteristics and meets users' diverse reading needs with the theory of user profile,the academic users to describe an omni-directional,multilevel and characterization,accurately reflect the characteristics of the users,meet the demand of users diverse reading,build a multi-dimensional academic user profile model,and the collaborative filtering algorithm is formed on the basis of academic resources recommendation algorithm based on user's profile.2)in this thesis,in view of the present research work more and more the trend of the interdisciplinary cooperation,it excavates the potential interest of users from the psychology the user's cognitive style,interdisciplinary academic resources to measure the academic user demand,expand academic resources recommended factors,further perfect the theory of personalized recommendation system of academic information resources.3)to adopt multi-disciplinary research,combines statistics,psychology,library and information science,data mining and other computer-related technologies,adopts questionnaire survey,empirical analysis and other research methods,and integrates the research ideas of social science into the research of recommendation system,which is highly advanced and operable.