Abstract
Aiming at the problems that the traditional model is difficult to extract information features, difficult to learn deep knowledge, and cannot automatically and effectively obtain features, which leads to the problem of low recommendation accuracy, this paper proposes a personalized tourism route recommendation model of intelligent service robot using deep learning in a big data environment. Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. Finally, the processed data is input into the trained recommendation model to generate a personalized tourism route recommendation scheme. The experimental analysis of the proposed model based on Pytorch deep learning framework shows that its Pre@10, Rec@10 values are 88% and 83%, respectively, and the mean square error is 1.537, which are better than other comparison models and closer to the real tourist route of the tourists.
Highlights
With the continuous improvement of social living standards, people’s demand for tourism and leisure is increasing year by year. e development and prosperity of the tourism industry have made going out to travel increasingly popular
When users face massive network data, they cannot realize a quick selection of information [3, 4]. erefore, the author hopes to be able to automatically obtain personalized travel recommendations that meet their specific needs to help users quickly filter useless information in a large amount of travel information and improve the efficiency and comfort of users in integrating information [5]. e personalized route recommendation platform is diversified
Intelligent service robots in scenic spots occupy an important position among many platforms because of their high efficiency and convenience. erefore, its recommended algorithm model is very important
Summary
With the continuous improvement of social living standards, people’s demand for tourism and leisure is increasing year by year. e development and prosperity of the tourism industry have made going out to travel increasingly popular. Reference [15] proposed a route recommendation method based on interest topic and distance matching, which obtains the best travel path by analyzing the user’s real historical travel footprint and scenic spot residence time and combined with the given travel time limit This method has poor timeliness and adaptability and cannot be applied to the independent intelligent robot platform. To fuse multisource heterogeneous data, the proposed model uses the Gaussian kernel function, node2vec model, and other technologies to construct the embedded representations of users, time, space, POI score, access frequency, and social relationships Send it to the deep learning network for analysis. Erefore, in the user’s personalized recommendation, combined with the theme factors of POI, more effective features are obtained from the limited user access information, and appropriate models are selected to achieve distinguishable user preference modeling. High-level interactions between the features are learned through neural networks, and personalized recommendations are made to users
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