Research On Knowledge Recommendation Of Open Innovation Community Based On Fuzzy Concept Lattice And Collaborative Filtering

In the age of information explosion,companies are working hard to solve the problem of how to tap the potential interests of users and provide personalized services to enhance user satisfaction and loyalty,thereby enhancing the competitiveness of enterprises and helping enterprises to obtain higher commercial value.In the current situation of fierce market competition,the key to improve the competitiveness of enterprises lies in improving the knowledge innovation ability of enterprises.Open innovation community has become one of the important ways for enterprises to acquire innovative knowledge because it can make full use of the wisdom of the crowd.However,the increasing amount of information in the enterprise open innovation community is increasing the cognitive burden of users.Users often spend a lot of time and effort to find and screen the information and knowledge they really need or are interested in,which greatly hinders the community knowledge innovation that users participate in.This paper takes the open innovation community as the research object.The mechanism and implementation method of personalized knowledge recommendation in open innovation community based on fuzzy concept lattice and collaborative filtering were explored.The construction process of user-item fuzzy concept lattice was presented,and a personalized knowledge recommendation algorithm based on fuzzy concept lattice and collaborative filtering(FCLCF)was proposed.The MovieLens open data set was used as the experimental data set to compare the proposed algorithm with the traditional user-based collaborative filtering recommendation algorithm and itembased collaborative filtering recommendation algorithm.Taking the users innovation community of domestic famous mobile phone as an example,the user behavior data and relevant text content of the community were extracted,and the interest and preference of users on the corresponding knowledge points were obtained by using the text mining.Combined with previous studies to determine the leading user identification indexes,and identified the leading users in the open innovation community based on these indexes,and then constructed the fuzzy concept lattice of leading user-knowledge point.Finally,FCLCF algorithm was used to present an ordered knowledge recommendation list for the leading users to meet their personalized needs.Experiments show that the algorithm proposed in this paper is superior to the traditional user-based collaborative filtering recommendation and item-based collaborative filtering recommendation in the classification accuracy and sorting accuracy.Personalized recommendation for users in the open innovation community not only meets users' personalized knowledge needs,but also helps to improve users' satisfaction and loyalty to the community,enhance users' continuous willingness to create and share knowledge,and promote the sustainable development of the open innovation community.In addition,it is beneficial for enterprises to obtain more knowledge and ideas shared by users that are not easy to get and improve their knowledge innovation ability.