Bipartite graph recommendation method based on key user and time context

A key user and recommendation method technology, applied in data processing applications, business, instruments, etc., can solve the problems of not considering the degree of contribution, reducing recommendation accuracy, and high computational complexity, so as to ensure real-time performance, reduce computing scale, and improve The effect of accuracy

Inactive Publication Date: 2018-05-15
HOHAI UNIV
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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, result

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  • Bipartite graph recommendation method based on key user and time context
  • Bipartite graph recommendation method based on key user and time context
  • Bipartite graph recommendation method based on key user and time context

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Embodiment Construction

[0026] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0027] Bipartite graph recommendation methods based on key users and temporal contexts, such as figure 1 shown, including the following steps:

[0028] Step 1: collect user feedback data on the product. In the recommendation system, the user's feedback data contains the user's historical interest preferences, which can be explicit methods such as the user's direct rating, voting, tagging, and commenting, or implicit methods such as the user's purchase, collection, or browsing behavior. ;

[002...

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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 ...

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Application Information

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IPC IPC(8): G06Q30/06
CPCG06Q30/0631G06Q30/0609
Inventor 翁小兰王志坚徐会艳
Owner HOHAI UNIV
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