A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Giovanni Pilato, Fabio Persia, Mouzhi Ge, Theodoros Chondrogiannis, D. D’Auria
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引用次数: 0

Abstract

Orienteering or itinerary planning algorithms in tourism are used to optimize travel routes by considering user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-of-Interest (POIs) or tourist routes. However, nowadays, user preference has been significantly affected by COVID-19 since health concern plays a key trade-off role. For example, people may try to avoid crowdedness, even if there is a strong desire for social interaction. Thus, the orienteering or itinerary planning algorithms should optimize routes beyond user preference. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, personality, knowledge of COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with a properly trained fastText neural network and a set of specialized Naïve Bayesian Classifiers based on the “Yelp!” data set. Also, we demonstrate how to approach and integrate COVID-related factors via conversational agents. Furthermore, the proposed system is in a modular design and evaluated in a user study; thus, it can be efficiently adapted to different algorithms for COVID-19-aware itinerary planning.
面向新冠肺炎时代个性化定向运动的模块化社会传感系统
旅游中的定向或行程规划算法用于通过考虑用户偏好和其他约束(如时间预算或交通状况)来优化旅行路线。对于这些算法,探索用户偏好来预测潜在的兴趣点(poi)或旅游路线是至关重要的。然而,如今,用户偏好受到COVID-19的重大影响,因为健康问题起着关键的权衡作用。例如,人们可能会尽量避免拥挤,即使有强烈的社会互动的愿望。因此,定向或路线规划算法应该优化路线超越用户偏好。因此,本文提出了一种社会感知系统,该系统考虑了用户偏好与各种因素之间的权衡,如拥挤程度、个性、COVID-19知识、POI特征和社交欲望。实验使用经过适当训练的fastText神经网络和一组专门的Naïve贝叶斯分类器来分析用户兴趣。数据集。此外,我们还演示了如何通过会话代理处理和整合与covid相关的因素。此外,所提出的系统采用模块化设计,并在用户研究中进行评估;因此,它可以有效地适应不同的算法进行covid -19感知行程规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Management Information Systems
ACM Transactions on Management Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.30
自引率
20.00%
发文量
60
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