A Grid Portal Recommendation Model Based on Collaborative Filtering

Grid portal is playing an important role in grid computing area. However, with the large-scale diverse resource need to be dispatched and coordinated, the portal shows its insufficient ability to deal with this complex situation and can not bear the overload of long-time transaction querying. What is worse, the users can not get their desired or expected resources. To solve these problems, grid portal recommendation architecture is presented. It consists of collaborative filter interaction layer, action layer and user render layer. In addition, a 2-way combo collaborative filter algorithm is put forward, and then the algorithm comparison is shown. Finally the experiment results improve that this architecture can be used to obtain the expected portal recommendation function and guarantee quality of personalized recommendation.