Tourism Recommendation System (TRS) systems address the needs of the tourist by examining a few factors. In order to make a foolproof recommendation, a variety of factors need to be taken into consideration, including environmental factors, exact geocoordinates, trip destination, preferences of tourists, etc. Various Artificial (AI) techniques have been developed, draw backs of these techniques are spatiotemporal characteristics, user privacy and data secrecy were not concentrated, traffic information, etc., Recently, importance has been given to the development of tourism infrastructure. Existing techniques failed in considering the demographic factors, which produced invalid results. Thus, in this paper, a tourism TRS is proposed using the Non-Central Chi-Squared Distribution-based Deep Learning Neural Network (NC-DLNN) classification technique is developed using the Shapefile, Google External Application Programming Interface (API), and Geographic Information System (GIS) map details are stored in the Geodatabase, Direction-based Fire Hawks Optimization (D-FHO) filtering, Alignment-based Bidirectional Encoder Representations from the Transformers (A-BERT) technique. The proposed method achieves 97.91% of accuracy, 97.9% of precision and 97.92% of specificity. Furthermore, the proposed embedding algorithm achieves a better Bleu Score value.
Key words: Rapid Automatic Keyword Extraction(RAKE), Geographic Information System(GIS), Direction-based Fire Hawks Optimization (D-FHO), Alignment-based Bidirectional Encoder Representations from the Transformers (A-BERT), Quintic Interpolation(QI), Non-Central Chi-Squared Distribution-based Deep Learning Neural Network(NC-DLNN), Application Programming Interface(API).
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