基于序列兴趣提取和位置信息融合的旅游景点推荐模型

IF 0.6 4区 工程技术 Q4 MATERIALS SCIENCE, TEXTILES AATCC Journal of Research Pub Date : 2023-05-09 DOI:10.1177/24723444231172228
Manman Zhang, Ruijia Tong, Xiaoling Xia
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引用次数: 0

摘要

旅游,作为一种放松自己的方式,已经成为现代社会人们享受身心的首选。然而,在面对大量信息的同时,如何通过用户的历史兴趣点来帮助用户更好地决定下一步的旅行目标,是大数据推荐系统需要进一步研究的方向。在本文中,我们提出了深度卷积和多头自注意位置网络模型。首先,利用卷积神经网络方法提取用户历史兴趣点特征信息,然后进行横向和纵向滤波;然后,将获取的信息与候选吸引力信息交互,通过多头自注意机制提取历史兴趣序列的位置信息。最后,该模型通过融合位置信息的特征信息,对候选吸引力的注意机制进行了研究。最终模型实现了用户序列兴趣和位置特征信息的深度融合。我们在不同的公开数据集上与业界非常流行的模型进行了详细的对比实验,结果表明我们的深度卷积和多头自关注位置网络模型具有良好的性能。
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A Tourist Attractions Recommendation Model Based on Sequence Interest Extraction and Location Information Fusion
Travel, as one way to relax oneself, has become the first choice for people to enjoy their body and mind in modern society. However, while facing lots of information, how to help users make better decisions on their next travel goals through their historical interest spots is a direction that needs further research in big data recommendation systems. In this thesis, we proposed the deep convolution and multi-head self-attention position network model. First, it extracts the user’s historical interest point feature information by convolutional neural network method, and then performs horizontal and vertical filtering. Next, it interacts the obtained information with the candidate attraction information, and extracts the location information of the historical interest sequence by the multi-head self-attention mechanism. Finally, the model does the attention mechanism of the candidate attraction by fusing the feature information of the location information. The final model achieves a deep fusion of user sequence interest and location feature information. We conducted detailed comparison experiments with the very popular models in the industry on different public datasets, and the results showed that our deep convolution and multi-head self-attention position network model has good performance.
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来源期刊
AATCC Journal of Research
AATCC Journal of Research MATERIALS SCIENCE, TEXTILES-
CiteScore
1.30
自引率
0.00%
发文量
34
期刊介绍: AATCC Journal of Research. This textile research journal has a broad scope: from advanced materials, fibers, and textile and polymer chemistry, to color science, apparel design, and sustainability. Now indexed by Science Citation Index Extended (SCIE) and discoverable in the Clarivate Analytics Web of Science Core Collection! The Journal’s impact factor is available in Journal Citation Reports.
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