LAUSR

The rise in business and electronic transactions using the web as a medium has driven the widespread use of recommendation systems across all domains. Recommendation systems generate suggestions for the travel and tourism industry, particularly hotels or services, centered on user interests and preferences. Relevant information collection, organization, processing vast quantities of string data, efficient data retrieval, and computational complexity in handling information overload are key issues to be addressed in a recommendation system. This work proposes a novel approach, using encoded bit vectors, to process information on hotel amenities and features extracted from Tripadvisor. Hotel amenities at different locations are organized and stored, based on the priority of the location, using the most significant bit and least significant bit. The amenities are prioritized dynamically thereafter, based on the location, and unique amenities accorded greater preference than the ones routinely offered at most hotels. Encoded bit vectors are utilized for information retrieval and precise hotel recommendations made, based on user interest rather than the string processing method that is ineffective where scalable data are concerned. Finally, the recommended hotels are ranked with user-centric personalized recommendations. These high-speed, efficient, and scalable algorithms are ideal for incrementally updating data. They also facilitate fast, flawless IR and query processing, along with appropriate hotel recommendations. The theoretical evaluation shows that the proposed method aids quick query processing and retrieval. The experimental assessment shows that hotels recommended by the proposed bit vector recommendation system have a higher accuracy on par with user and expert recommendations.