An Architecture for Collaborative Filtering in Grid Portal Recommendation System

In order to solve such problems as over-scale of the grid portal resources management, heavy-load of handing with large-scale querying and processing, low-satisfaction of the users who need access to get the desired the resources, we present an improved grid portal recommendation architecture in this paper. The proposed architecture in combination with a collaborative filtering algorithm allows for the main features of the grid portal. In addition, the paper implements the architecture efficiently through a prototype portal in which both action layer and render layer are designed for collaborative filtering. Lastly the experiment results show that the adoption of this architecture can be used to achieve grid portal personalized collaborative filtering recommendation function and guarantee accuracy and quality of personalized recommendation.