E-government recommendation algorithm based on probabilistic semantic cluster analysis in combination of improved collaborative filtering in big-data environment of government affairs

In order to solve the problem of information overload and satisfy their individual needs in the use of e-government system, a personalized e-government recommendation algorithm combining probabilistic semantic cluster analysis and collaborative filtering is proposed to recommend the items most associated with users. Firstly, the static basic attributes and dynamic behavior attributes of the users and items are modeled by probabilistic semantic cluster analysis separately. Secondly, based on the user’s historical record and attribute similarity for user community discovery, the user set most similar to the target user is pre-filtered by collaborative filtering algorithm to improve the diversity of the recommended results and reduce the computational amount of the core recommendation process. Finally, the associated sequence mining of the items was taken full account of the business characteristic of e-government, and the item sequence mining with time dimension was added to further improve the accuracy of the recommended results. The simulation experiments were carried out with the information after desensitization of users on the Spark platform. The experimental results show that our proposed e-government recommendation algorithm is suitable for the recommendation of items with sequence or process characteristic and has higher recommendation accuracy compared with traditional recommendation algorithms. The multi-community attribution factor of the user increases the diversity of the recommended results.