Personalized scenic spot recommendation method and device based on knowledge graph and user's long-term and short-term preferences

Abstract

The present invention proposes a personalized scenic spot recommendation method based on knowledge graph and user's long-term and short-term preferences, including: preprocessing the tourist's historical scenic spot sequence and performing scenic spot-coding conversion; using node2vec random walk to obtain the scenic spot sequence, using word2vec The Skip-gram model in to obtain the feature vectors of tourists and scenic spots; the feature vectors of scenic spots plus bias will be used as the input of the GRU network, and then use the GRU network to train and output the potential vector of each scenic spot; for each scenic spot Assign different weights, multiply the weight of each scenic spot with the potential vector of the scenic spot and accumulate to obtain the long-term preference of the current tourist, and then splicing the long-term preference of the current tourist with the current preference of the tourist and multiplying by the weight to obtain the final vector; Perform the dot product operation between the final vector and the current preferences of tourists to obtain the estimated rating of the scenic spots, and normalize the estimated ratings of the scenic spots to get the predicted probability of each scenic spot, and take the scenic spots corresponding to the top K scores to get the top_k scenic spot recommendations list.

Description

technical field [0001] The present invention relates to technical fields such as intelligent recommendation, in particular to a personalized scenic spot recommendation method and device based on knowledge graphs and long-term and short-term preferences of users. Background technique [0002] With the improvement of the consumption level of the Chinese people, the people's demand for traveling is increasing day by day. According to relevant data, the number of people traveling is on the rise. Recommendation system technology has also emerged, but most of the current recommendation systems cannot fully meet the individual needs of users. The specific reason is that the existing travel recommendation methods only construct user preference feature representations based on the user's historical visit location, so as to make personalized recommendations. However, the travel interests of users may change over time. Therefore, the existing scenic spot recommendation methods are d...

AI Technical Summary

Problems solved by technology

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a personalized scenic spot recommendation method and device based on knowledge graphs and users' long-term and short-term preferences, so as to solve the problem of low recommendation accuracy and personalized recommendation results in current deep learning. low level problem

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