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.

以网络为媒介的商务和电子交易的兴起推动了推荐系统在所有领域的广泛使用。推荐系统以用户的兴趣和喜好为中心，为旅游业，尤其是酒店或服务业提供建议。相关信息收集，组织，处理大量字符串数据，有效的数据检索以及处理信息过载的计算复杂性是推荐系统中要解决的关键问题。这项工作提出了一种新颖的方法，使用编码的位矢量来处理有关从Tripadvisor提取的酒店设施和功能的信息。根据位置的优先级，使用最高有效位和最低有效位来组织和存储不同位置的酒店设施。此后，根据位置动态地对设施进行优先排序，并且与大多数酒店通常提供的设施相比，独特的设施具有更大的偏爱。编码的位向量用于信息检索和精确的酒店推荐，基于用户的兴趣，而不是在涉及可伸缩数据时无效的字符串处理方法。最后，以用户为中心的个性化推荐对推荐酒店进行排名。这些高速，高效且可扩展的算法非常适合增量更新数据。它们还促进快速，完美的IR和查询处理，以及适当的酒店建议。理论评估表明，该方法有助于快速查询处理和检索。