College of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, Republic of Korea.
Comput Intell Neurosci. 2022 Apr 28;2022:7115627. doi: 10.1155/2022/7115627. eCollection 2022.
The information age of rapid development of tourism industry provides abundant travel information, but it also comes with the problem of information overload, which makes it difficult to meet the growing personalized needs of people. The traditional collaborative filtering recommendation algorithm (CFA) also suffers from the problem of data sparsity when the user population increases. Therefore, this study optimizes the CFA through the similarity factor and correlation factor and enhances the tourism sense of travel experience through the satisfaction balance strategy. The experimental results show that the improved CFA method has the highest average accuracy on the overall dataset and the best recommendation performance of the satisfaction balance strategy. Overall, the recommendation model in this study is useful for attraction selection of users and marketing optimization of travel companies.
旅游业信息时代的快速发展提供了丰富的旅游信息，但也带来了信息过载的问题，这使得人们越来越难以满足个性化需求。传统的协同过滤推荐算法（CFA）在用户群体增加时也存在数据稀疏的问题。因此，本研究通过相似度因子和相关度因子对 CFA 进行了优化，并通过满意度平衡策略增强了旅游体验的旅游感。实验结果表明，改进的 CFA 方法在整体数据集上具有最高的平均准确性，并且满意度平衡策略的推荐性能最佳。总体而言，本研究中的推荐模型对用户的景点选择和旅游公司的营销优化是有用的。
