Chen Wei
School of Hotel and Tourism Management, Shunde Polytechnic University, Foshan, Guangdong, 528333, China.
Sci Rep. 2024 Dec 4;14(1):30237. doi: 10.1038/s41598-024-75794-3.
This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists' purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists' attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists' purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists' purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists' willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers' needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields.
本研究基于影响游客购买行为的因素建立了一个深度学习模型，用于提供个性化的旅游推荐，以便为用户提供更准确、个性化的旅游推荐。首先，使用自然语言处理（NLP）技术对旅游评论信息进行处理和情感分析，将其分为正面、负面或中性，以了解游客对购买产品和服务的态度。其次，基于影响游客购买行为的因素构建了一个高性能网络（HPN）模型。游客、产品和口碑（WOM）之间的关系被表示为一个复杂网络，以分析和预测旅游电子口碑（EWOM）数据中的事件发生模式和影响因素。模型的构建考虑了各种因素，如口碑传播、价格影响等，以揭示游客、口碑、产品等之间的复杂关系。最后，将循环神经网络（RNN）模型与反向传播（BP）模型相结合，借助门控循环单元处理时间序列数据，并对HPN模型进行训练和评估。使用Yelp数据集验证模型的准确性和可行性，该数据集包含许多旅游目的地的评分和评论数据。结果表明，价格、口碑和目的地是影响游客购买行为的主要因素之一，其中口碑最为显著。正面的口碑评论显著提高产品销量，而负面口碑则产生相反的效果。年龄、职业、教育程度、个人月收入和游客购买意愿的最小期望为0.00，性别因素的最小期望为0.31。RNN-BP混合模型具有更高的准确性和预测能力，比单一模型和传统机器学习预测模型的准确率分别高出1.73%和2.30%。简而言之，本研究有助于更好地理解旅行者的需求和偏好，以优化产品和服务，提高市场竞争力。此外，本研究的方法和模型也可应用于其他领域的EWOM数据挖掘。
