Collaborative Optimization For Collaborative Editing System Of Zhejiang Publishing Group

This paper aims to optimize collaborative of Zhejiang Publishing Group Collaborative editing system, to reduce the publishing costs, improve the quality of the publication and rise the company’s competitive. This paper starts from two lowest synergistic nodes, do the following research:1. Using CO and genetic algorithms only to optimize the system, find out the limitations, then Using keywords gene coding optimization CO optimization results from the static target value into the optimal feasible region, then using genetic algorithm further approach the optimal solution, through the above way integrating the two algorithms advantages into a CO-genetic composite optimization algorithm.2. Using existing collaborative filtering algorithm to the system, find the limitations, then combining content recommendation algorithm and modified cosine similarity method, work out an algorithm which both Contain the user feedback and the user interest function. By using the above method, work out a complex collaborative filtering algorithm.3. By using the two composite optimization algorithms, we build a key gene pool, a database and a new information sharing platform. Through experimental group comparison method, we tested optimized results.By comparison of the experimental data, we find two links coordination has been improved significantly. But with the change of environment coordination, system need to improving, for example, pricing, checking and other aspects of collaborative optimization has a very wide research space.