Collaborative filtering processor

Abstract

The invention discloses a collaborative filtering processor. A method includes the following steps: adopting an improved K-means algorithm to perform clustering on users; selecting a cluster formed by the users similar to a target user in attribute; acquiring a nearest neighbour set of the target user; generating a recommendation set. The main idea includes that the users identical in feature are clustered into a class through clustering to lower dimensionality of a matrix and reduce spatial complexity, sparse matrices are filled through matrix decomposition, and then collaborative filtering is performed on the filled matrices.

Description

technical field [0001] The invention relates to the technical field of recommendation, and more specifically to a collaborative filtering algorithm processor based on k-means clustering. Then, the sparse matrix is ​​filled by matrix decomposition, and collaborative filtering is performed on the filled matrix. [0002] Background technique [0003] With the popularization of the Internet and the rapid development of computer technology, the amount of information has exploded, and the personalized recommendation system has become an increasingly concerned research field after search engines. With the research of scholars, more and more recommendation algorithms have been proposed, including: content-based recommendation, collaborative filtering recommendation algorithm and combined recommendation algorithm. [0004] As the most successful recommendation technology, collaborative filtering has been practically applied in many fields, but there are still many problems to be so...

AI Technical Summary

Problems solved by technology

The disadvantage of the neighborhood-based algorithm is that when the matrix is ​​very sparse, it is difficult for the algorithm to find similar users for the target user, so that the recommendation quality is greatly limited

Images