A Hybrid Recommendation Algorithm Adapted In Integration Of Informatization And Industrialization For Industrial Enterprises

In 2007, the Communist Party of China (CCP) put forward the concept of integration of informatization and industrialization. At The 5th plenary meeting in the 17th central committee of Party, the Central Committee of CCP proposed speeding up the process of integration of informatization and industrialization to improve the development of the informatization of economic society by industry upgrading. In terms of the strategy of integration of informatization and industrialization, our party and country has already realized the importance of industrialization and informationization. Therefore, a knowledge platform for integration of informatization and industrialization was built to solve the problem of knowledge reserve. As the rapid development of integration of informatization and industrialization, the exponential growth of the available case in the platform causes the information overload problem, which refers that user cannot quickly and accurately locate the case they need.To address this issue, this paper introduce a framework based on the Assessment system of integration of informatizaion and industrialization and user learning behavior. First, the similarity model of integration of informatization and industrialization for industrial enterprises was established based on the Assessment specification on integration of informatization and industrialization for industrial enterprises. Second, the similarity model of user behavior was built based on three kind of learning behaviors in the knowledge platform. After studied the advantages and disadvantages of both methods, this paper proposed a linear fusion framework to effectively combine both algorithms.The experimental results show that the proposed framework achieves the better recommendation quality.