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

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...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the current personalized recommendation system has low computational efficiency and insufficient system scalability when faced with massive data.

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