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Hotel Recommendation System using advanced efficiency and accuracy with modified BERT technique

Discovering compatible hotel for customer’s needs and economy is a tough process. These days, it helps with the availability of a sufficient amount of customer-created online reviews. This fact also helps consumers improve information processing by presenting the recommended hotel system and the direction of promising research in the tourism field. This proposal system employs hotel sentiment analysis as part of its hotel recommendation system. Although RoBERTa and BERT have almost identical architectures, the RoBERTa model exceeds the BERT model in the context of performance. Robustly Optimized BERT Pretraining Approach (RoBERTa) model is used to segregate reviews into positive, negative, and neutral. Actual reviews show different sentiments of hotel guests, and each review categorization is done considering multiple categories like location, cleanliness, service, etc. The way reviews function with user-supplied queries determines how they are categorized. The reviews are grouped using cosine similarity and fuzzy logic simultaneously. The resulting dataset is stored into Apache Kafka. In consideration of the issue of inconsistent data flow in real application scenarios, the message queue Kafka was introduced. It guarantees that real-time data input rates are smooth and consistent. In this paper, implementation of a real-time recommendation system using micro-batch data stream processing by Kafka-Spark integration is done. It uses Spark Streaming to ensure minimal data loss while concurrently storing all Kafka data for speedy recovery.