| With the arrival of knowledge economy,the traditional Closed Innovation cannot meet the needs of the rapid development of the market.Enterprises begin to use the concept of Open Innovation for better management.The open innovation community has become an important mechanism of enterprise innovation.Users are the main body of open innovation community.By encouraging users to participate in community activities and then generate content,external innovation resources can be transformed into enterprises.The open innovation community can understand the user needs,improve the user experience and promote user innovation by analyzing the user behavior and segmenting users.This study takes the user behavior data of the open innovation community as the sample,analyzes the characteristics of user interaction behavior and constructs the user profile system based on the complex network theory and RFM model.This study then clusters the user groups,and addresses the user segmentation.This study starts with the concept of the open innovation community,and put in order the literature review concerning user behavior in open innovation and user profile.Through the analysis of relevant literature in the field of library and information science,this study combines complex network theory and RFM model to build the framework of user interaction behavior analysis,further develops the label system of user behavior profile in the context of open innovation community from the dimensions of user behavior habits,social networks and interest preferences.The research ideas of user profile are then clarified.This study chooses Xiaomi community as the sample of empirical analysis.First,the crawler code is written to capture the user behavior data.After data preprocessing,this study conducts statistical analysis,social network analysis and interest preference based on the user behavior data to understand the characteristics of user interaction behavior,and explore the distinction between users.The results show that the number of user-liked and comments obey the power-law distribution,and there are obvious distinctions in the distribution of comment length,comment time,comment content and emotional preference.Through complex network analysis,it can be found that the user interaction network conforms to the characteristics of small world network model,and the centrality of each user has significant differences.Combined with k-shell,high influence users and low influence edged users can be distinguished.Through the analysis of the characteristics of user interaction behavior in open innovation community,combined with the construction principle of user profile and RFM model,user loyalty,monetary,sentiment and influence are selected as the clustering indicators,and six principal components are obtained by dimensionality reduction.The user group is divided into six categories by K-means++ clustering algorithm,namely " Large following authoritative user","social star user" "Loving interaction user","community development user","edged user with long comments" and "silent supporting user" respectively.This study explores the differences in the characteristics of each user group,and uses the knowledge of management to offer reasonable suggestions for the open innovation community from the perspective of community operation and accurate recommendation.Based on the situation of open innovation community,this study analyzes the characteristics of user interaction behavior and constructs user profile.It expands the application scenario of complex network theory and RFM model,and widens the research perspective of user behavior in open innovation community.By exploring the differences between each user group,the needs and preferences of different user groups can be more accurately found.This study helps the open innovation community to better understand the needs of users,and provide relevant references for the operation and development direction of the open innovation community. |
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