Abstract: Combining the context-aware recommender system with Bayesian networks,we proposed a context-aware recommendation algorithm,and designed a context-aware Resource Recommendation System Architecture．First using the Bayesian network,through computing the joint probability distribution of user access time and resource information,user interest of resources in the environment was obtained,and then the similarity of current user environment selected resources and past user environment selected resources was compared to provide appropriate resources list．Finally we compared the proposed algorithm with other common recommender system algorithms,and at the same time,the system architecture was modeled according to the M/G/1queue.The result proves the superiority and stability of our system architecture and the algorithm are better.

Key words: Recommender system,Context,Bayesian network,Expectation-maximization