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Southeast Asia is one of the most popular tourist destinations in the world, and its tourism model is mainly based on ecological and sustainable tourism characteristics. It is a special practical exploration of this region. However, with the rapid development of the tourism industry, the structural contradictions in the tourism industry have become increasingly prominent, coupled with the weak link of tourism development, making the development of tourism unbalanced. Therefore, this study proposed a platform-related algorithm based on machine learning to build the integration of eco-sustainable tourism and marketing in Southeast Asia and used a collaborative filtering algorithm and a route generation algorithm to recommend information or predict specific needs for specific tourists. The experimental results of this study showed that the difference between different loss functions of the improved transfer algorithm based on machine learning is not significant, which indicated that the method proposed in this study is robust to the choice of a loss function. Compared with the hinge loss function and the logarithmic loss function, the exponential loss function has the highest average classification accuracy of 0.91, of which the average accuracy of the logarithmic loss function is 0.85, and the average classification accuracy of the hinge loss function is 0.79. Therefore, applying the migration algorithm to the architecture of the platform is conducive to the stability of the dataset and to the better display of pages for tourists. [ABSTRACT FROM AUTHOR]

Smart tourism recommendation refers to supplementing suggestions for tourists' self-service sightseeing plans based on Internet technology, and recommending more effective and practical tourism information. This paper uses big data and artificial intelligence algorithms to study the smart tourism recommendation method in Southeast Asia. First of all, this paper gives a brief overview of the definition and classification of big data and artificial intelligence algorithms and then introduces tourism resources and smart tourism in Southeast Asia. Finally, this paper conducts a comparative experiment between the smart tourism development model based on big data and artificial intelligence algorithms and the traditional tourism development model and analyzes the utility of the development model from three aspects, namely, tourists' sense of experience and satisfaction, the scale of tourism transactions and the growth rate of tourism revenue, and the adequacy and harmony of tourism resource allocation. The final experimental results show that the overall average value of tourists' experience and satisfaction under the smart tourism recommendation mode based on big data and artificial intelligence algorithms is 88.84 points, which is 7.30 points higher than the traditional tourism mode, which verifies its effectiveness. [ABSTRACT FROM AUTHOR]

This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. REFERENCES 1 Chen Y., Li Y., Tseng T. Construction of Integrated Platform for Eco-Sustainable Tourism and Marketing in Southeast Asia Based on Machine Learning. [Extracted from the article]