Multimedia information recommendation method and device and storage medium

Abstract

The invention provides a multimedia information recommendation method and device and a storage medium, and the method comprises the steps: determining sample multimedia information which comprises the sample multimedia information and user feedback information corresponding to the sample multimedia information; clustering the sample multimedia information to generate N clusters; storing the sample multimedia information meeting a preset condition in the N clusters into a target matrix; determining a neighbor multimedia information set according to a similarity value between the sample multimedia information in the target matrix and the target multimedia information; the neighbor multimedia information set comprises at least one piece of sample multimedia information of which the similarity value is greater than a first preset value; determining the matching degree between the neighbor multimedia information in the neighbor multimedia information set and the target multimedia information; determining the first preset number of neighbor multimedia information corresponding to the maximum matching degree as recommended multimedia information; the recommended multimedia information is pushed to a target user, and the target user is a user who pays attention to the target multimedia information.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a multimedia information recommendation method, device and storage medium. Background technique [0002] In the prior art, multimedia information recommendation methods are generally divided into memory-based recommendation methods and model-based recommendation methods according to the difference of data loaded into memory at runtime. The memory-based recommendation method is to directly call the entire user data when the algorithm is running, and perform calculations on the entire data set in real time to determine the multimedia information to be recommended; while the model-based recommendation algorithm can be divided into a learning phase and a recommendation phase. In the learning stage, the recommendation model is established through the data set offline, and then the recommendation model is transferred into the memory in the recommendation stage to determine...

AI Technical Summary

Problems solved by technology

The memory-based recommendation method is to directly call the entire user data when the algorithm is running, and perform calculations on the entire data set in real time to determine the multimedia information to be recommended; while the model-based recommendation algorithm can be divided into a learning phase and a recommendation phase. In the learning stage, the recommendation model is established through the data set offline, and then the recommendation model is transferred into the memory in the recommendation stage to determine the multimedia information to be recommended; but when the two multimedia information recommendation methods determine the multimedia information to be recommended, Both require a large amount of data calculation and matching, and high-dimensional data and vector calculations will lead to reduced recommendation accuracy, heavy workload, and low efficiency.

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