Scenic spot recommendation method and device based on hybrid supervised learning

Abstract

The invention provides a scenic spot recommendation method based on hybrid supervised learning. The scenic spot recommendation method comprises the steps of obtaining historical tourist touring data;constructing a scenic spot knowledge graph; performing corresponding attribute sub-graph extraction on the scenic spot knowledge graph according to the attribute category of the scenic spot; generating a scenic spot sequence; training the scenic spot sequence and mapping the scenic spot sequence into a low-dimensional vector space to generate a feature vector; adding and averaging the vectors of each scenic spot under different attributes to obtain a fused semantic feature vector of each scenic spot; learning tourist vectors and scenic spot potential vectors; carrying out matrix decompositionon the tourist vector and the fused semantic features to obtain a first interaction vector; obtaining a second interaction vector of the tourist vector and the scenic spot potential vector by using amulti-layer perceptron; splicing the first interaction vector and the second interaction vector and performing normalization processing to obtain a score of the tourist for the scenic spot; ranking the scores of the tourists for the scenic spots from high to low, and obtaining a top _ k scenic spot recommendation list by taking the first K scenic spots with the highest scores.

Description

technical field [0001] The present invention relates to technical fields such as machine learning, knowledge graph, and intelligent recommendation, and in particular to a method and device for recommending tourist attractions based on hybrid supervised learning. Background technique [0002] According to relevant data from the China Tourism Administration, in recent years, the number of Chinese people traveling has been increasing year by year. People's demand for personalized travel recommendations is growing. The purpose of the recommendation system is to help users recommend products they may like based on user preferences and product characteristics, thereby improving recommendation efficiency and user satisfaction. Although traditional recommendation systems can provide users with some recommendations, it is difficult for users to make choices in the face of massive travel information recommendations. Therefore, a travel recommendation system that can make accurate re...

AI Technical Summary

Problems solved by technology

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method and device for recommending tourist attractions based on hybrid supervised learning to solve the problems of low recommendation accuracy and low personalization of recommendation results in current deep learning.

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