偏好融合的旅游群组景点推荐:基于深度学习的框架模型  

Attraction Recommendation for Tourist Groups with Preferences Fusion: A Deep Learning-Based Framework Model

作　　者：朱志国[1,2] 孙怡 王谢宁 万校基[3] ZHU Zhiguo;SUN Yi;WANG Xiening;WAN Xiaoji(School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian 116025;Key Laboratory of Liaoning Province for Data Analytics and Decision-Making Optimization,Dalian 116025;Business School,Huaqiao University,Quanzhou 362021)

机构地区：[1]东北财经大学管理科学与工程学院,大连116025 [2]辽宁省大数据管理与优化决策重点实验室,大连116025 [3]华侨大学工商管理学院,泉州362021

出　　处：《系统科学与数学》2025年第2期470-484,共15页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金项目(72172025,72101051);教育部人文社科规划基金项目(21YJAZH130);辽宁省教育厅基本科研项目(LJKMZ20221606);辽宁省社会科学规划基金项目(L21BTQ001)资助课题。

摘　　要：随着“马蜂窝”等社交媒体的广泛兴起,更多的游客从传统标准的“跟团游”转向灵活自由的“结伴游”.不同于传统的用户个性化推荐,为类似的旅游群组精准推荐景点时,如何更好融合众多成员的异质旅游偏好,已成为当前旅游行业中具有重要实践价值的热点研究问题.为此,文章首先采用局部异常点检测方法识别兴趣差异较大的离群用户,初步进行旅游群组的聚类.接下来,基于先进的深度学习方法,提出神经协同旅游群组推荐模型ANC-TGR.该模型通过巧妙设计的“项目级”和“用户级”两层注意力神经网络,准确聚合群组的共同旅游偏好,并进一步输入至一个神经协同过滤推荐框架中,捕捉群组与项目间的高阶交互关系.最终,为旅游群组进行Top-N的景点推荐服务.实验结果证实,提出的模型ANC-TGR由于进一步优化融合旅游群组的偏好表示,与最优的基准模型相比,在Mafengwo(有群组)和Foursquare(无群组)两个数据集中,在HR@10和NDCG@10指标上,分别提升了10.45%和10.48%,以及10.07%和10.87%.文章为提高旅游群组的景点推荐准确性和出行满意度提供了技术方法支持.With the rise of social media such as“Mafengwo”,more and more tourists have become increasingly inclined toward the more flexible and freer self-organizing“tour group”rather than the traditional and standard“tour packages”.Different from traditional individual personalized recommendation,it has become a hot issue with important practical value that how to better aggregate the heterogeneous tour preferences of members for accurate tourist group recommendation.To this end,the method of Local Outlier Factor(LOF)is firstly adopted for data preprocessing to identify the outlier users with large differences in tour interests,and then the tourist groups can be preliminarily clustered.Next,the model ANC-TGR(attention-based neural collaborative tourist group recommendation)is proposed.In this model,the tour preference representation of a tourist group can be accurately aggregated through a well-designed two-layer attention network of“item-level”and“user-level”,and the representation vector is further input into a neural collaborative filtering recommendation framework for accurately recommending the Top-attractions for the tourist group.In the datasets of Mafengwo(with groups)and Foursquare(without groups),the experimental results confirm that the proposed model ANC-TGR,which further optimizes the preference representation of the fusion tourism group,compared with the optimal benchmark model,increased by 10.45%,10.48%,and 10.07%,10.87%on the metrics of HR@10 and NDCG@10,respectively.This paper provides technical support to improve the accuracy of attraction recommendations and travel satisfaction of tourism groups.

关 键 词：旅游推荐 群组推荐 深度学习 注意力机制 神经协同过滤推荐 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.3[自动化与计算机技术—控制科学与工程] F590[经济管理—旅游管理]