Cluster group discovery-based recommendation system and method and personalized recommendation system

A recommendation method, a technology of clustering algorithm, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of low computing efficiency and insufficient system scalability, and achieve the effect of improving scalability and accuracy

Active Publication Date: 2018-04-20
XIDIAN UNIV
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Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the current personalized recommendat

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  • Cluster group discovery-based recommendation system and method and personalized recommendation system
  • Cluster group discovery-based recommendation system and method and personalized recommendation system
  • Cluster group discovery-based recommendation system and method and personalized recommendation system

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Embodiment Construction

[0068] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0069] While ensuring the usability of the traditional recommendation system, the invention alleviates the scalability problem of the recommendation system in combination with technologies such as group discovery, similarity calculation, and clustering algorithm.

[0070] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0071] like figure 1 As shown, the cluster group discovery-based recommendation system provided by the embodiment of the present invention includes: a user behavior processing module 1 , a user-item group discovery module 2...

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Abstract

The invention belongs to the technical field of personalized recommendation and discloses a cluster group discovery-based recommendation system and method and a personalized recommendation system. According to collection of user operation behavior data by an online system, a user-project matrix is extracted; an entire user-project data set is divided into multiple subgroups that are highly internally correlated to each other; via combination of Euclidean and other similarity calculation methods and clustering algorithms, recommendation results are obtained after a collaborative filtering algorithm is used directly in the subgroups. Via use of the cluster group discovery-based recommendation system and method and the personalized recommendation system, data preprocessing operation is performed before a recommendation algorithm is used directly, fuzzy clustering is adopted for grouping the data, and a final recommendation result is obtained after predicted scores of all groups are integrated. While normal operation of the entire recommendation system is ensured, a fuzzy cluster group discovery-based data preprocessing method allows the recommendation algorithm to be applied directlyto individual groups of data that are strong in correlation instead of all data, and therefore the recommendation system can be improved in expansibility and accuracy in the face of mass data.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation, in particular to a recommendation system and method based on clustering group discovery, and a personalized recommendation system. Background technique [0002] In recent years, with the rapid development of the Internet, information explosion has become the norm. In particular, the recommendation system also includes the processing of implicit user behavior data, and makes targeted personalized recommendations for each user, and processes data on the server side. The capability requirements are getting higher and higher, and improving the scalability of many personalized systems has received extensive attention. Aiming at the problems of low computing efficiency and insufficient system scalability in the face of massive data in the current personalized recommendation system, ensuring the scalability of the recommendation system in the face of massive data is a hot topic of cu...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321
Inventor 裴庆祺王伟
Owner XIDIAN UNIV
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