Bipartite graph recommendation method based on key user and time context

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of personalized intelligent recommendation, and in particular relates to a bipartite graph recommendation method based on key users and time context. Background technique [0002] In recent years, recommendation algorithms based on bipartite graphs have been widely used in recommendation systems and become a research hotspot. Using the traditional bipartite graph recommendation method, regardless of the importance of users to the recommendation system, material resources are transferred in the entire user space, including a large part of users who have nothing to do with the target user's interest or have weak correlation, resulting in high computational complexity , it is difficult to guarantee the real-time performance of the algorithm. In addition, the traditional bipartite graph recommendation method does not consider the contribution of user evaluation time to the recommendation results, thus reducing ...

AI Technical Summary

Problems solved by technology

Using the traditional bipartite graph recommendation method, regardless of the importance of users to the recommendation system, material resources are transferred in the entire user space, including a large part of users who have nothing to do with the target user's interest or have weak correlation, resulting in high computational complexity , it is difficult to guarantee the real-time performance of the algorithm

In addition, the traditional bipartite graph recommendation method does not consider the contribution of user evaluation time to the recommendation results, thus reducing the recommendation accuracy

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