中文摘要：

针对网格计算环境下,参与计算用户和计算资源规模日益庞大,用户申请资源过程中所需的资源匹配过程逐步复杂化和大规模化,提出了一种基于推荐机制的网格资源匹配算法.以往的网格计算资源的匹配和调度算法需要在调度计算时遍历所有网格资源,而改进的基于SVD（奇异值分解）的协同过滤算法考虑了用户行为相关性和资源使用频度的相关性,通过用户对资源项的使用历史记录建立用户对资源的满意度评分体系,利用推荐机制给出用户推荐资源集以到达资源匹配的效果.从一个新的角度给出了解决大量资源匹配的方法.

英文摘要：

Focusing on the problem of applying and matching resources under large-scale users and computing resources in grid environment, a kind of recommendation-based grid resource matching algorithm is presented. Many existing grid resource matching and scheduling algorithms have to search and compare every grid computing resource node without considering features of grid resources and users＇ behaviors, while recommendation system as widely used means in e-commerce could solve all of these two problems well. To utilize recommendation mechanism could pretreat information of users and resources by translating features of grid resources to eigenvectors of items in recommendation system and setting up a satisfaction grade system considering history records with features described in resources applying process that reflect the users＇ behaviors through the frequency users computed in resource nodes. Then, the authors improve SVD-based （singular value decomposition） collaborative filtering algorithm that can give users recommendation resource sets by computing the best approximate resource features to users＇ behavior features matrix. Especially, the grid resource matching algorithm could mine latent features from given data, efficiently overcome the extreme sparsity of user satisfaction grade data and make use of feedback information from resources scheduling. The problem of matching a mass of resources is solved in a novel way from a new perspective.