Personalized tourist attraction recommending method based on knowledge domains map

Abstract

The invention discloses a personalized tourist attraction recommending method based on a knowledge domains map. The personalized tourist attraction recommending method based on the knowledge domains map comprises the following steps: constructing a tourist area knowledge domains map through a massive amount of data on the internet; coding information in the knowledge domains map through an improved TransE model; training attractions and user nodes into a n-dimensional vector (which is assumed to have n attributes) according to the number of link attributes; also showing the relation between users and attractions as an n-dimensional vector; after vector representation of the users and the attractions, calculating similarity of the users and similarity of the attractions; substituting the similarity into a prediction scoring formula to obtain two prediction scores; then normalizing difference between vectors calculated by f (h, r, t) to the range between scoring threshold values to obtain a third prediction score; and finally, carrying out weighted averaging on the three prediction scores to obtain a final scoring list which is used for recommending tourist attractions for the users.By the personalized tourist attraction recommending method based on the knowledge domains map, the problems of poor semanteme, low recommending accuracy and cold start in the prior art are solved, and the practicality is good.

Description

technical field [0001] The invention relates to the technical field of knowledge graph and machine learning, in particular to a method for recommending personalized tourist attractions based on knowledge graph. Background technique [0002] In recent years, the rapid development of cloud computing, Internet of Things, mobile Internet, artificial intelligence and other technologies has brought a lot of convenience to people's work and life. In terms of tourism and leisure, users can easily search for tourism information and purchase tourism products and services through the Internet, enjoying the convenience brought by information technology. However, when faced with the explosive growth of network information, it is difficult for users to make efficient choices. The emergence of recommender systems provides an effective way to solve information overload. The recommendation system is a subset of the information filtering system, which aims to predict the user's preference f...

AI Technical Summary

Problems solved by technology

[0004] What the present invention aims to solve is the problem of low recommendation accuracy existing in existing personalized recommendation methods and the inherent cold start of the recommendation system, which cannot be applied to the recommendation of tourist attractions, and provides a personalized recommendation of tourist attractions based on knowledge graphs method

Images