Research On Personalized Tourist Attractions Recommendation Based On Sequence Mining

The rapid popularity and development of the Internet are constantly changing people's way of lifestyle,becoming an indispensable part of modern society,and bringing great convenience to the life and work of people.The rapid development of Internet technology has introduced the involvement of a large number of users,which makes the amount of data on the Internet increase at the speed of exponent.The long time and high cost of retrieval have become a common problem troubling users to retrieve the tourism information that they are interested in and make tourism planning.The recommendation system can effectively reduce the data number and help users to find tourism information that matches their preferences.In the field of tourism recommendation,although the traditional recommendation algorithms have a good recommendation effect,they are still confronted with some problems in the processing of big data,such as sparse data,inaccurate feature extraction,and ignoring the semantic information in the user access sequence,which cannot accurately learn the representation of users and scenic spot features,resulting in the problem of low recommendation accuracy.In view of the improvement of these problems,this paper designed recommendation algorithms for tourist attractions based on sequential mining to improve the accuracy of landmark recommendation.The specific work is as follows:(1)Aiming at the problem of ignoring the complex semantic information of the access attractions sequence in the recommendation methods based on sequence modeling,we propose a recommendation method combining graph representation learning and sequence mining(GRL-SM)in this paper.In this method,graph neural network is used to model the sequences of access attractions to obtain the semantic information of complex transition between attractions.In addition,this model takes into account the characteristic that users' preferences change with time,and uses the attention mechanism to acquire the users' long-term and short-term preferences contained in the attractions sequence,and further obtains the accurate preferences of users,thus provides personalized recommendation services for tourist attractions.(2)Aiming at the problem of ignoring the context information and position information(the order that appears in the sequence)of the access attractions sequence in the recommendation methods based on sequence modeling,we propose a personalized landmark recommendation method based on context and position information(F-CPI)in this paper.In this method,the attraction in the sequence is rerepresented by the attention mechanism,and the attraction vector introducing context information is generated.Besides,the position information of attractions in the sequence is modeled by introducing the position vector.then the two vectors are combined to calculate the attention weight of each attraction in the generated sequence,finally the preference feature representation of the user is obtained.This model introduces both the context information and the position information of attractions to improve the recommendation performance,and has a good performance on the real tourism data set.