Recommendation System for Sustainable Day and Night-Time Cultural Tourism Using the Mean Signed Error-Centric Recurrent Neural Network for Riyadh Historical Sites

To accommodate user-specific requirements and preferences, a travel Recommendation System (RS) gives a customized place of interest. The prevalent research did not provide solutions to some essential situations for cultural tourism, including relevant time, environmental conditions, and stay places. Thus, the existing RS models led to unreliable cultural tourism recommendations by neglecting essential factors like personalized itineraries, environmental conditions of the cultural sites, sentiment analysis of the hotel reviews, and sustainable cultural heritage planning. To overcome the above factors, a day- and night-time cultural tourism RS utilizing the Mean Signed Error-centric Recurrent Neural Network (MSE-RNN) is proposed in this paper. The proposed work develops an efficient RS by considering historical data, Geographic Information System (GIS) map location, hotel (stay place) reviews, and environmental data to access day and night cultural tourism. First, from the Geographic Information System (GIS) map and hotel data, the historical and hotel geolocations are extracted. Currently, these locations are fed to Similarity-centric Hamilton Distance-K-Means (SHD-KM) for grouping the nearest locations. Next, hotels are ranked utilizing the Tent Mapping-centric Black Widow Optimization (TM-BWO) approach centered on the locations. In addition, using Bidirectional Encoder Representations from Transformers (BERT), the essential keywords from the historical geo-locations are embedded. In the meantime, the sites’ reviews and timings are extracted from Google. The extracted reviews go through pre-processing, and the keywords from the pre-processed data are extracted. For the extracted keywords, polarity is calculated centered on the Valence-Aware Dictionary for Sentiment Reasoning (VADER). Concurrently, utilizing the Reference-centric Pearson Correlation Coefficient (R-PCC), the timings of the sites are segregated. Lastly, for providing a recommendation of tourist sites, the embedded words, ranked hotels, and segregated timings, along with the pre-processed environment and season data, are fed to the MSE-RNN classifier. At last, the experimental evaluation verified that other recommendation systems were surpassed by the proposed approach.