Adaptive coevolution algorithm-based information kernel extraction method

Abstract

The invention discloses an adaptive coevolution algorithm-based information kernel extraction method, and mainly solves the problem of incapability of ensuring an optimal recommendation effect in theprior art. The method is implemented by the steps of (1) constructing a user score matrix; (2) initializing parent populations; (3) adaptively adjusting a cross operator selection probability; (4) adaptively adjusting a mutation operator selection probability; (5) classifying the parent populations; (6) building a team; (7) judging whether each team member comes from a parent elite population or not; (8) updating progeny elite populations; (9) updating progeny ordinary populations; (10) judging whether all information kernels of the parent elite population are central information kernels or not; (11) updating the parent populations; (12) judging whether an iterative frequency is equal to 200 or not; and (13) outputting an optimal information kernel. An experimental simulation result showsthat compared with an existing information kernel extraction method, the method provided by the invention has a better recommendation effect.

Description

technical field [0001] The invention belongs to the technical field of physics, and further relates to an information core extraction method based on an adaptive co-evolutionary algorithm in the technical field of item recommendation. The present invention can extract the information core through an adaptive co-evolutionary algorithm according to the user's scoring information on the item, thereby guiding the user to find the item they need according to the information core, thereby improving the efficiency of online recommendation and solving the scalability problem of traditional collaborative filtering . Background technique [0002] The advent of the era of big data has led to an increasingly serious problem of information overload. How to quickly and effectively obtain information of interest from massive amounts of information has become an urgent problem to be solved. Faced with the above problems, the recommendation system came into being and received extensive atte...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that using the similarity to construct an artificially set information core selection standard has certain subjectivity, and it cannot guarantee that the obtained information core has the best recommendation effect.

Although this method uses the evolutionary algorithm to extract the information core, the disadvantage of this method is that when using the evolutionary algorithm, the traditional genetic algorithm framework is used, which is insufficient in search efficiency and can be further improved.

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