Ding Jie, Weng Lingyan, Fan Lili, Liu Peixue
School of Tourism, Nanjing Institute of Tourism and Hospitality, Nanjing, Jiangsu, China.
School of Business Administration, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China.
PeerJ Comput Sci. 2024 Oct 9;10:e2329. doi: 10.7717/peerj-cs.2329. eCollection 2024.
Tourism demand projection is paramount for both corporate operations and destination management, facilitating tourists in crafting bespoke, multifaceted itineraries and enriching their vacation experiences. This study proposes a multi-layer self attention mechanism recommendation algorithm based on dynamic spatial perception, with the aim of refining the analysis of tourists' emotional inclinations and providing precise estimates of tourism demand. Initially, the model is constructed upon a foundation of multi-layer attention modules, enabling the semantic discovery of proximate entities to the focal scenic locale and employing attention layers to consolidate akin positions, epitomizing them through contiguous vectors. Subsequently, leveraging tourist preferences, the model forecasts the likelihood of analogous attractions as a cornerstone for the recommendation system. Furthermore, an attention mechanism is employed to refine the spatial layout, utilizing the forecasted passenger flow grid to infer tourism demand across multiple scenic locales in forthcoming periods. Ultimately, through scrutiny of data pertaining to renowned tourist destinations in Beijing, the model exhibits an average MAPE of 8.11%, markedly surpassing benchmarks set by alternative deep learning models, thereby underscoring its precision and efficacy. The spatial layout optimization methodology predicated on a multi-layer attention mechanism propounded herein confers substantive benefits to tourism demand prognostication and recommendation systems, promising to elevate the operational standards and customer contentment within the tourism sector.
旅游需求预测对于企业运营和目的地管理都至关重要，有助于游客制定定制化、多维度的行程安排并丰富他们的度假体验。本研究提出了一种基于动态空间感知的多层自注意力机制推荐算法，旨在细化对游客情感倾向的分析并提供精确的旅游需求估计。首先，该模型基于多层注意力模块构建，能够对焦点景区附近的实体进行语义发现，并利用注意力层合并相似位置，通过连续向量对其进行概括。随后，利用游客偏好，该模型预测类似景点的可能性，作为推荐系统的基石。此外，采用注意力机制来优化空间布局，利用预测的客流网格推断未来时期多个景区的旅游需求。最终，通过对北京著名旅游目的地相关数据的审查，该模型的平均平均绝对百分比误差（MAPE）为8.11%，明显超过其他深度学习模型设定的基准，从而突出了其准确性和有效性。本文提出的基于多层注意力机制的空间布局优化方法为旅游需求预测和推荐系统带来了实质性好处，有望提高旅游行业的运营标准和客户满意度。
