Abstract
This paper expounds relevant theoretical research on personas and the advantages of the annual reading report of academic college library based on personas, and expounds the construction of personas in detail from four aspects: data collection, behavior modeling, construction personas and visual presentation. The annual reading report of the academic college library with personas digs in depth all kinds of reading data in the library, constructs personas model by using a set of tags, objectively understands the reader's reading tendency, and facilitates librarians to carry out accurate reading promotion services.
Highlights
In recent years, in the context of the era of big data, libraries have creatively applied technologies such as big data and artificial intelligence to explore the potential needs of readers, established a multi-dimensional interaction model between libraries and readers to provide readers with more personalized and humanized services
This paper expounds relevant theoretical research on personas and the advantages of the annual reading report of academic college library based on personas, and expounds the construction of personas in detail from four aspects: data collection, behavior modeling, construction personas and visual presentation
The annual reading report of the academic college library with personas digs in depth all kinds of reading data in the library, constructs personas model by using a set of tags, objectively understands the reader's reading tendency, and facilitates librarians to carry out accurate reading promotion services
Summary
In the context of the era of big data, libraries have creatively applied technologies such as big data and artificial intelligence to explore the potential needs of readers, established a multi-dimensional interaction model between libraries and readers to provide readers with more personalized and humanized services. Personas are based on massive data, extracting the overall information related to the users, including inherent attributes, such as users’ names, ages, genders, and the users’ reading habits. Such information provides the basis for further analyzing the users’ behavior habits by big data and for more accurate users targeting and personalized services [1]. In terms of practical application, through the massive survey of the 2017 reading reports published by the university libraries, it is found that in the “double top-class” university libraries in China, only the reading reports from Peking University Library [7] and the Sun Yat-sen University Library [8] revealed the readers’ data through personas. The reading reports found different reading tendencies of different readers through their body physical characteristics and found that there was a positive correlation between font size and reader preference
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