[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121865-en":3,"doc-seo-121865-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121865,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Hybrid Travel Recommender Model Based on Deep Level Autoencoder And Machine Learning Algorithms","This research investigates using autoencoders to process Malayalam travelogues shared on Facebook for building a hybrid travel recommender. The work addresses the lack of a Malayalam benchmark travel dataset by applying NLP preprocessing to unstructured, long, and imbalanced travelogues, adding filtering steps, and creating a dedicated Part of Travel Tagger (POT Tagger) with lookup dictionaries. A two-stage pipeline encodes travelogues into a low-dimensional latent space, then decodes to reconstruct inputs before training multiple machine learning classifiers. Models trained on encoded features achieve higher accuracy than conventional approaches, reaching 95.84% validation accuracy, demonstrating improved recommendation accuracy and efficiency for low-resource Malayalam users.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 Issue 5 Year 2023 Page 1220-1229  \nA Hybrid Travel Recommender Model Based on Deep Level AutoencoderAnd  \nMachine Learning Algorithms  \nMuneerV.K1*, Mohamed Basheer K.P2  \n1*, 2Sullamussalam Science College, Affiliated to University of Calicut, Kerala  \n[vkmuneer@gmail.com](vkmuneer@gmail.com1)[1](vkmuneer@gmail.com1)  \n*Corresponding Author: Muneer V.K  \n*Sullamussalam Science College, Affiliated to University of Calicut, Kerala  \n[vkmuneer@gmail.com](vkmuneer@gmail.com)  \n\n| Article History\u003Cbr>Received-20 September 2023 Revised-17 October 2023 Accepted: 22 November 2023 | Abstract\u003Cbr>This research investigates the application of autoencoders in processing travelogues written in the Malayalam language on Facebook. The main objective is to harness the capabilities of autoencoders to learn a compressed representation of the input data and employ it to train various machine learning models for enhanced accuracy and efficiency. The major challenge of unavailability of a benchmark dataset in the Malayalam language for the travel domain was overcome by employing NLP techniques on the unstructured, lengthy, imbalanced travelogues, applying some additional filtering methods, and the creation of an exclusive Part of Travel Tagger (POT Tagger) along with lookup dictionaries. As this pioneering work focuses on Malayalam travel reviews posted on social media, the model presents a valuable opportunity for extension to other low-resourced Indian languages. The study follows a two-step approach. Initially, an autoencoder neural network architecture is utilized to encode the travelogues into a lowerdimensional latent space representation. The encoder network adeptly captures crucial features and patterns within the data. The compressed representation obtained from the encoder is then fed into the decoder, which reconstructs the original travelogues. Subsequently, the encoded model is employed to train diverse machine learning models, including logistic regression, decision tree classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbours (KNN), stochastic gradient descent (SGD), and multilayer perceptron (MLP) . By utilizing the encoded features as inputs, these models effectively learn from the concise representation of the Malayalam travelogues. Experimental results reveal that the trained machine learning models, using the encoded features, achieve higher accuracy rates compared to conventional approaches. This improvement demonstrates the effectiveness of autoencoders in capturing and representing vital characteristics of the Malayalam travelogues on Facebook. By leveraging capabilities of autoencoder model, we successfully learned a compressed representation of the input data, attaining an impressive validation accuracy of 95.84% . This finding highlights the potential of autoencoders to enhance the overall accuracy and efficiency of travel recommendation systems for Malayalam users on social media platforms. |\n| --- | --- |\n\nAvailable online at: [https://jazindia.com](https://jazindia.com) 1220  \nJournal of Advanced Zoology  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Keywords: Autoencoders, self-supervised learning, Recommender model, Natural language processing, Malayalam. |\n| --- | --- |\n\n.  \n1. Introduction  \nTravel and tourism play a pivotal role in promoting cultural exchange, economic growth, and personal enrichment. With the exponential growth of social media platforms and online travel communities, individuals now have unprecedented access to a wealth of travel-related information and experiences shared by fellow travellers. Among these platforms, Facebook travel groups have emerged as dynamic hubs for travellers to document their journeys, share captivating travelogues, and exchange valuable insights and recommendations. In this research paper, we focus on harnessing the vast potential of Facebook travel groups to develop a personalized travel","cbCaieonSdLjPR1r","https://ap.wps.com/l/cbCaieonSdLjPR1r","pdf",608123,1,10,"English","en",105,"# Introduction\n## Social media travel groups and personalized recommendations\n## Malayalam language challenges and dataset creation\n## Data cleaning, feature extraction, and travel recommender foundation\n## Autoencoder-based representation learning\n## Encoding, decoding, and training of machine learning models","[{\"question\":\"What problem does the paper address in travel recommendations for Malayalam users?\",\"answer\":\"It targets the lack of robust NLP resources and benchmark datasets for Malayalam travel text, enabling accurate personalized travel recommendations from Facebook travelogues.\"},{\"question\":\"How does the proposed model use autoencoders in the pipeline?\",\"answer\":\"It encodes travelogues into a low-dimensional latent space, reconstructs the original text through decoding, and then uses the encoded representation to train downstream machine learning classifiers.\"},{\"question\":\"Which machine learning models are trained using the autoencoder-derived features?\",\"answer\":\"The paper trains logistic regression, decision tree classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbors (KNN), stochastic gradient descent (SGD), and multilayer perceptron (MLP).\"}]","A Hybrid Travel Recommender Model Based on Deep Level Autoencoder And Machine Learning Algorithms | 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problem does the paper address in travel recommendations for Malayalam users?","Question",{"text":75,"@type":76},"It targets the lack of robust NLP resources and benchmark datasets for Malayalam travel text, enabling accurate personalized travel recommendations from Facebook travelogues.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed model use autoencoders in the pipeline?",{"text":80,"@type":76},"It encodes travelogues into a low-dimensional latent space, reconstructs the original text through decoding, and then uses the encoded representation to train downstream machine learning classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are trained using the autoencoder-derived features?",{"text":84,"@type":76},"The paper trains logistic regression, decision tree classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbors (KNN), stochastic gradient descent (SGD), and multilayer perceptron 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