Interest point recommendation method based on user positive and negative preference learning

Abstract

The invention provides an interest point recommendation method based on user positive and negative preference learning, and the method comprises the steps: learning deep features of interaction between a user and a scenic spot through employing a neural network, and training two neural network models: a positive preference neural model and a negative preference neural model; wherein the positive preference neural network model generates a list of scenic spots loved by the user, and then acquiring a final recommendation list through optimization of the negative preference neural network model,so that more accurate scenic spot recommendation is provided for the user. The problems that traditional interest point recommendation precision is not high, and the individuation degree of recommendation results is low are solved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a point-of-interest recommendation method based on user positive and negative preference learning. Background technique [0002] In recent years, with the rapid development of cloud computing, Internet of Things, mobile Internet, artificial intelligence and other technologies, it has brought a lot of convenience for people to go out, such as watching movies, dining and traveling. However, the emergence of various applications in the Internet space has triggered an explosive growth of data. How to obtain valuable information from the complicated data and recommend suitable points of interest for users is particularly important. The traditional location-based recommendation is mainly based on the popularity statistics of points of interest or combined with the characteristics of users and points of interest. However, it takes a long time to process complex data, and it can only l...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a point-of-interest recommendation method based on the user's positive and negative preference learning, using the neural network to learn the deep-level characteristics of the interaction between the user and the scenic spot, and training two neural network models at the same time, the positive preference neural model and the negative preference neural model. model; the positive preference neural network model generates a list of attractions that users like, and then optimizes through the negative preference neural network model to obtain the final recommendation list to provide users with more accurate scenic spot recommendations; Issues such as low degree of personalization of results

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