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This study investigates the use of autoencoders to analyse travelogues posted in Malayalam on Facebook. The main goal is to use autoencoders to provide a compact representation of the input data, which will be used to train different machine learning models to improve accuracy and efficiency. To address the lack of a standardized dataset in Malayalam for the tourist sector, NLP techniques need to be used to unstructured, long, and uneven travel logs. This is handled by using extra filtering methods and using a specialized POS Tagger in conjunction with lookup dictionaries. The study used a two-step technique. An autoencoder neural network architecture is used to encode the travelogues into a lower-dimensional latent space representation. The encoder’s compressed representation is transmitted to the decoder to reconstruct the original travelogues. The encoded model is utilized for training several machine learning models, including MLP, logistic regression, RFC, SVM, KNN, and SGD. The model attains a remarkable validation accuracy of 96.84%.

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An Ensembled Travel Recommender Model Using Autoencoder and Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • V. K. Muneer

摘要

This study investigates the use of autoencoders to analyse travelogues posted in Malayalam on Facebook. The main goal is to use autoencoders to provide a compact representation of the input data, which will be used to train different machine learning models to improve accuracy and efficiency. To address the lack of a standardized dataset in Malayalam for the tourist sector, NLP techniques need to be used to unstructured, long, and uneven travel logs. This is handled by using extra filtering methods and using a specialized POS Tagger in conjunction with lookup dictionaries. The study used a two-step technique. An autoencoder neural network architecture is used to encode the travelogues into a lower-dimensional latent space representation. The encoder’s compressed representation is transmitted to the decoder to reconstruct the original travelogues. The encoded model is utilized for training several machine learning models, including MLP, logistic regression, RFC, SVM, KNN, and SGD. The model attains a remarkable validation accuracy of 96.84%.