Design And Implementation Of Open Source Community Content Recommendation System Based On User Information Fusion

The development of the Internet allows users to obtain a huge amount of information.When faced with a huge amount of information,Internet users need to filter out the content they are interested in.However,information overload makes it difficult for users to find what they need from a large amount of information,which greatly reduces the efficiency of information use.The emergence of recommendation algorithms provides new ideas for information filtering.The algorithm helps users quickly filter information by predicting the content of the target user's interest,thereby improving the efficiency of information use.At this stage,the recommendation algorithm is widely used,including social media,video and audio,e-commerce shopping,advertising,etc.,but the recommendation of open source community content is still under development.At present,there are three problems in open source community content recommendation: first,the reference dimensions for content recommendation for users in the open source community are not diverse enough;second,the connection between different open source communities is not close enough;third,the mobile application of open source communities is not mature enough.Starting from the above problems,this article,based on the existing work,combined with user information fusion ideas,mixed recommendation methods and random walk strategies,studied the open source community content recommendation algorithm based on user information fusion,and combined it with i OS mobile development to design,realized the mobile terminal open source community content recommendation system.The detailed work content and implementation plan are as follows:1.User information fusion starts from multiple dimensions,integrates the same user information between different open source communities,similar user information in open source communities,and user personal information to construct a user-user relationship matrix and a user-tag relationship matrix.2.Using a random walk strategy to update the user-user matrix and user-tag matrix,reducing the subjectivity of the relationship between open source community users and interest tags.Improve user interest ranking scoring strategy,make the scoring results more accurate,and improve the objectivity of the relationship between open source community users and interest tags.3.The performance of the algorithm proposed in the article is compared with the contentbased recommendation algorithm and the collaborative filtering recommendation algorithm,and the algorithm accuracy,F-measure value,recall rate,etc.are compared,which proves the open source community content recommendation based on user information fusion The performance of the algorithm is better than the other two recommendation algorithms,and it has a better recommendation effect.4.Combine the recommendation algorithm with mobile application development,use Apple's Core ML framework to integrate the machine learning model,and deploy the algorithm training model to the i OS mobile terminal,so that the model can interact with other controls in the mobile development process.On this basis,combined with i OS mobile development knowledge,an open source community content recommendation system is implemented,which provides a feasible solution for the development of open source community content recommendation on the mobile terminal.