Personalized recommendation method based on dynamic neighboring point spectral clustering

Abstract

The invention relates to a personalized recommendation method based on dynamic neighboring point spectral clustering. A user-store bipartite network is set up according to user sign-in information; the user-store bipartite network is projected to a user-user one-aside network and a store-store one-aside network, and a node2vec algorithm is used for projecting the two weighted one-aside networks totwo different vector spaces; the spectral clustering algorithm based on dynamic neighboring points is called to cluster user vectors and store vectors obtained above to obtain multiple user clustersand multiple store clusters; the sign-in information existing between single users is converted into a cluster network among the user clusters and the store clusters; a K-means algorithm is used for dividing the one-dimension vector into two classes, the store clusters in the class with the larger sign-in average number are recommended to the user clusters; personalized recommendation is conductedaccording to each user cluster and the recommended store clusters. The accuracy of the recommendation method is improved effectively.

Description

technical field [0001] The invention belongs to the field of recommendation methods, and in particular relates to a personalized recommendation method based on dynamic adjacent point spectrum clustering. Background technique [0002] Recommendation techniques include content-based and knowledge-based recommendations, collaborative filtering recommendations, and so on. Content-based and knowledge-based recommendations are based on the information of the object content and do not need to rely on the user's rating of the store. Collaborative filtering recommendation can recommend for users to find people with similar preferences or stores similar to their favorite stores. In most recommendation systems, users have few evaluation or consumption records for items, which makes the user-item rating matrix have very few rating records. When looking for similar users for target users, data sparseness has become the biggest obstacle. It directly affects the accuracy of the recommend...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the difficulties in extracting network features in the existing clustering recommendation algorithm, the difficulty in determining the clustering center in the clustering algorithm, the poor clustering effect and the inability to recommend store clusters for user clusters, the present invention provides a clustering effect A personalized recommendation method based on dynamic neighborhood spectral clustering that is better, realizes personalized recommendation, and recommends store clusters for user clusters

Images