Based on the features and current status of the Teaching Center for Experimentation and Practice,Tsinghua University,Shenzhen,the reasons which lead to low utilization rate of teaching experimental equipment are analyzed,such as few experimental hours,lack of standardized management system and security support platform. The way of establishing teaching experiment equipment evaluation mechanism is explored to improve the strict management system.Through building an easy operation evaluation mechanism,the existing experimental equipments are evaluated,and it provides a guidance on the effective use of equipment. Meanwhile,we improve the management mechanism: perform assessment before procurement,strengthen the management of the entire process. The measures include standardizing daily use,standardized platforms and building security and support platform,strengthening the team of equipment managers and inspecting the equipment assets regularly. A combination of the both achieves the efficiency,optimizes resource allocation and sets the foundation for long term development of the teaching center.
Collaborative filtering is one of the most extensive and successful personalized recommendation algorithm in e-commerce recommendation system.Affected by data sparsity,the traditional collaborative filtering algorithms does not reflect the interest similarity of uses calculating similarity between users on the smaller set of common rated items accurately,seriously affecting the accuracy of recommendation system.To solve this problem,collaborative filtering algorithm based on co-ratings was proposed by analyzing the distribution of co-ratings and relationship between co-ratings and similarity,directly using co-ratings as a criterion to select nearest neighbor without calculating similarity.Experiments on MovieLens datasets show that the algorithm can make a substantial increase in prediction accuracy and recommendation coverage.
