The invention discloses a
bipartite graph recommendation method based on a key user and a time context, which belongs to the technical field of personalized intelligent recommendation. The method comprises steps of acquisition of feedback data of a user to a goods, extraction of a key user collection, building of an interest preference neighbor collection of the user, material resource
diffusion on a
cut user-goods
bipartite graph and final recommendation. By adopting the method, the key
user group that plays a leading role in the recommendation
system is mined, an interest nearest neighbor collection C for the target user is found out in the group, the
bipartite graph is
cut according to the collection C, nodes and edges in no relation or weak relation with the target user are removed, the calculation complexity is thus reduced, and the real-time performance of the recommendation
algorithm is ensured. Besides, during the material
diffusion process in the second step, a user
score timedecay function is introduced, the different contribution degrees of different time scores to the recommendation results are reflected, and the
algorithm recommendation accuracy is thus improved.