research-article

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

Authors:

Giovanni Pilato,

Theodoros Chondrogiannis,

Daniela D’AuriaAuthors Info & Claims

ACM Transactions on Management Information Systems, Volume 14, Issue 4

Article No.: 31, Pages 1 - 26

https://doi.org/10.1145/3615359

Published: 26 October 2023 Publication History

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 tradeoff 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 article proposes a social sensing system that considers the tradeoff 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!” dataset. 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.

References

[1]

2014. Yelp! Dataset. Retrieved August 30, 2022 from https://www.yelp.com//dataset

[2]

2015. IPIP. Retrieved August 30, 2022 from https://ipip.ori.org/

[3]

2015. Yelp Categories. Retrieved August 30, 2022 from https://blog.yelp.com/community/yelp/

[4]

2020. TextBlob tool. Retrieved December 30, 2022 from https://textblob.readthedocs.io/en/dev/

[5]

2021. Langdetect tool. Retrieved August 30, 2022 from https://pypi.org/project/langdetect/

[6]

2022. CDC. Retrieved August 30, 2022 from https://www.cdc.gov/coronavirus/2019-ncov/faq.html

[7]

2022. NLTK tool. Retrieved December 30, 2022 from https://www.nltk.org/

[8]

2022. Rivescript. Retrieved August 30, 2022 from https://www.rivescript.com/

[9]

Abdullah Alamri. 2021. Semantic-linked data ontologies for indoor navigation system in response to covid-19. ISPRS Int. J. Geo-Inf. 10, 9 (2021), 607.

[10]

Saad Aljubayrin, Jianzhong Qi, Christian S Jensen, Rui Zhang, Zhen He, and Zeyi Wen. 2015. The safest path via safe zones. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’15). 531–542.

[11]

Ben Athiwaratkun, Andrew Gordon Wilson, and Anima Anandkumar. 2018. Probabilistic fasttext for multi-sense word embeddings. arXiv:1806.02901. Retrieved from https://arxiv.org/abs/1806.02901

[12]

Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc.

[13]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Trans. Assoc. Comput. Ling. 5 (2017), 135–146.

[14]

Paolo Bolzoni, Sven Helmer, Kevin Wellenzohn, Johann Gamper, and Periklis Andritsos. 2014. Efficient itinerary planning with category constraints. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 203–212.

Digital Library

[15]

Antonio Chella, Marcello Frixione, and Salvatore Gaglio. 2008. A cognitive architecture for robot self-consciousness. Artif. Intell. Med. 44, 2 (2008), 147–154.

Digital Library

[16]

Franco Chiavetta, Giosue Lo Bosco, and Giovanni Pilato. 2016. A lexicon-based approach for sentiment classification of amazon books reviews in italian language. In International Conference on Web Information Systems and Technologies, Vol. 3. Scitepress, 159–170.

[17]

Theodoros Chondrogiannis and Mouzhi Ge. 2019. Inferring ratings for custom trips from rich GPS traces. In Proceedings of the ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (SIGSPATIAL’19). 1–4.

Digital Library

[18]

Alfredo Cuzzocrea and Giovanni Pilato. 2020. An innovative user-attentive framework for supporting real-time detection and mining of streaming microblog posts. Soft Comput. 24, 13 (2020), 9663–9682.

Digital Library

[19]

Donatella Darsena, Giacinto Gelli, Ivan Iudice, and Francesco Verde. 2022. Sensing technologies for crowd management, adaptation, and information dissemination in public transportation systems: A review. IEEE Sens. J. (2022).

[20]

Paul Ekman. 1992. An argument for basic emotions. Cogn. Emot. 6, 3-4 (1992), 169–200.

[21]

Jean-Claude Falmagne, Mathieu Koppen, Michael Villano, Jean-Paul Doignon, and Leila Johannesen. 1990. Introduction to knowledge spaces: How to build, test, and search them. Psychol. Rev. 97, 2 (1990), 201.

[22]

Ram Deepak Gottapu and Lakshmi Venkata Sriram Monangi. 2017. Point-of-interest recommender system for social groups. Proc. Comput. Sci. 114 (2017), 159–164. Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS October 30 - November 1, 2017, Chicago, Illinois, USA.

[23]

Aldy Gunawan, Hoong Chuin Lau, and Pieter Vansteenwegen. 2016. Orienteering problem: A survey of recent variants, solution approaches and applications. Eur. J. Operat. Res. 255, 2 (2016), 315–332.

[24]

Siddharth Gupta, Deep Borkar, Chevelyn De Mello, and Saurabh Patil. 2015. An e-commerce website based chatbot. Int. J. Comput. Sci. Inf. Technol. 6, 2 (2015), 1483–1485.

[25]

Gang Hu, Yi Qin, and Jie Shao. 2020. Personalized travel route recommendation from multi-source social media data. Multimedia Tools Appl. 79, 45-46 (2020), 33365–33380.

Digital Library

[26]

Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou, et al. 2017. Efficient softmax approximation for GPUs. In International Conference on Machine Learning. PMLR, 1302–1310.

[27]

Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. https://arxiv.org/abs/1607.01759

[28]

Xiaofeng Wang Kun Wang and Xuan Lu. 2021. POI recommendation method using LSTM-attention in LBSN considering privacy protection. Complex Intell. Syst. (2021).

[29]

Huayu Li, Richang Hong, Shiai Zhu, and Yong Ge. 2015. Point-of-interest recommender systems: A separate-space perspective. In Proceedings of the IEEE International Conference on Data Mining (ICDM’15), Charu C. Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, and Xindong Wu (Eds.). 231–240.

Digital Library

[30]

Kwan Hui Lim, Jeffrey Chan, Shanika Karunasekera, and Christopher Leckie. 2019. Tour recommendation and trip planning using location-based social media: A survey. Knowl. Inf. Syst. 60, 3 (2019), 1247–1275.

Digital Library

[31]

Alessio D Marra, Linghang Sun, and Francesco Corman. 2022. The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area. Transp. Policy 116 (2022), 258–268.

[32]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781

[33]

Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018. Advances in pre-training distributed word representations. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’18).

[34]

Ram Krishn Mishra, Siddhaling Urolagin, and J. Angel Arul Jothi. 2020. Sentiment analysis for POI recommender systems. In Proceedings of the 7th International Conference on Information Technology Trends (ITT’20). 174–179.

[35]

Sumit Mishra, Nikhil Singh, and Devanjan Bhattacharya. 2021. Application-based COVID-19 micro-mobility solution for safe and smart navigation in pandemics. ISPRS Int. J. Geo-Inf. 10, 8 (2021), 571.

[36]

Paromita Nitu, Joseph Coelho, and Praveen Madiraju. 2021. Improvising personalized travel recommendation system with recency effects. Big Data Min. Anal. 4, 3 (2021), 139–154.

[37]

Cristina Maria Păcurar, Ruxandra-Gabriela Albu, and Victor Dan Păcurar. 2021. Tourist route optimization in the context of Covid-19 pandemic. Sustainability 13, 10 (2021), 5492.

[38]

Fabio Persia, Sven Helmer, Sergejs Pugacs, and Giovanni Pilato. 2019. Social sensing for improving the user experience in orienteering. In Proceedings of hte IEEE 13th International Conference on Semantic Computing (ICSC’19). IEEE, 239–246.

[39]

Fabio Persia, Giovanni Pilato, Mouzhi Ge, Paolo Bolzoni, Daniela D’Auria, and Sven Helmer. 2020. Improving orienteering-based tourist trip planning with social sensing. Fut. Gener. Comput. Syst. 110 (2020), 931–945.

[40]

Giovanni Pilato and Ernesto D’Avanzo. 2018. Data-driven social mood analysis through the conceptualization of emotional fingerprints. Proc. Comput. Sci. 123 (2018), 360–365.

[41]

Giovanni Pilato, Fabio Persia, Mouzhi Ge, and Daniela D’Auria. 2022. Social sensing for personalized orienteering mediating the need for sociality and the risk of COVID-19. IEEE Trans. Technol. Soc. (2022), 1–1.

[42]

Giovanni Pilato, Roberto Pirrone, and Riccardo Rizzo. 2008. A kst-based system for student tutoring. Appl. Artif. Intell. 22, 4 (2008), 283–308.

Digital Library

[43]

Radim Řehůřek and Petr Sojka. 2010. Software framework for topic modelling with large corpora. In Proceedings of the LREC’10 Workshop on New Challenges for NLP Frameworks. ELRA, 45–50.

[44]

Shini Renjith, A. Sreekumar, and M. Jathavedan. 2020. An extensive study on the evolution of context-aware personalized travel recommender systems. Inf. Process. Manag. 57, 1 (2020).

Digital Library

[45]

Stuart J. Russell. 2010. Artificial Intelligence a Modern Approach. Pearson Education, Inc.

[46]

Dimitris Sacharidis, Panagiotis Bouros, and Theodoros Chondrogiannis. 2017. Finding the most preferred path. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’17). 1–10.

Digital Library

[47]

Xi Shao, Guijin Tang, and Bing-Kun Bao. 2019. Personalized travel recommendation based on sentiment-aware multimodal topic model. IEEE Access 7 (2019), 113043–113052.

[48]

Amarjit Singh. 2020. COVID-19 has changed our lives forever. Financ. Expr. (2020).

[49]

Giorgos Stamatelatos, George Drosatos, Sotirios Gyftopoulos, Helen Briola, and Pavlos S. Efraimidis. 2021. Point-of-interest lists and their potential in recommendation systems. Inf. Technol. Tour. 23 (2021), 209–239.

[50]

D. Wang. 2017. Data reliability challenge of cyber-physical systems. In Cyber-Physical Systems, Houbing Song, Danda B. Rawat, Sabina Jeschke, and Christian Brecher (Eds.). Academic Press, Boston, 91–101.

[51]

Dong Wang, Tarek Abdelzaher, and Lance Kaplan. 2015. A new information age. In Social Sensing, Dong Wang, Tarek Abdelzaher, and Lance Kaplan (Eds.). Morgan Kaufmann, Boston, 1–11.

[52]

Senzhang Wang, Jiannong Cao, Hao Chen, Hao Peng, and Zhiqiu Huang. 2020. SeqST-GAN: Seq2Seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Trans. Spatial Algor. Syst. 6, 4 (2020), 1–24.

Digital Library

[53]

Xiaoting Wang, Christopher Leckie, Jeffrey Chan, Kwan Hui Lim, and Tharshan Vaithianathan. 2016. Improving personalized trip recommendation by avoiding crowds. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16). 25–34.

Digital Library

[54]

Heitor Werneck, Nícollas Silva, Matheus Carvalho Viana, Fernando Mourão, Adriano C. M. Pereira, and Leonardo Rocha. 2020. A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web. ACM, New York, NY, 185–192.

Digital Library

[55]

Jerry S. Wiggins. 1996. The five-factor model of personality: Theoretical perspectives. https://www.amazon.de/-/en/Jerry-S-Wiggins/dp/157230068X

[56]

Danfeng Yan, Xuan Zhao, and Zhengkai Guo. 2018. Personalized POI recommendation based on subway network features and users’ historical behaviors. Wireless Commun. Mobile Comput. 2018 (2018), 1–10.

Digital Library

Cited By

Persia FGe MPilato GD’Auria DRafanelli ACostantini SDe Gasperis G(2024)Leveraging DALI to Refine Route Planning by Dynamically Avoiding Risky POIs2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00061(351-354)Online publication date: 5-Feb-2024
https://doi.org/10.1109/ICSC59802.2024.00061
Yan K(2024)Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification methodHeliyon10.1016/j.heliyon.2024.e3041310:9(e30413)Online publication date: May-2024
https://doi.org/10.1016/j.heliyon.2024.e30413
Li XKwon JPadmanabhan BZhang P(2023)Introduction to the Special Issue on IT-enabled Business Management and Decision Making in the (Post) Covid-19 EraACM Transactions on Management Information Systems10.1145/362799514:4(1-2)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3627995
Show More Cited By

Index Terms

A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications
    1. Decision support systems
    2. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 14, Issue 4

December 2023

114 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3630723

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2023

Online AM: 06 September 2023

Accepted: 02 August 2023

Revised: 07 July 2023

Received: 31 August 2022

Published in TMIS Volume 14, Issue 4

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Cited By

Persia FGe MPilato GD’Auria DRafanelli ACostantini SDe Gasperis G(2024)Leveraging DALI to Refine Route Planning by Dynamically Avoiding Risky POIs2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00061(351-354)Online publication date: 5-Feb-2024
https://doi.org/10.1109/ICSC59802.2024.00061
Yan K(2024)Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification methodHeliyon10.1016/j.heliyon.2024.e3041310:9(e30413)Online publication date: May-2024
https://doi.org/10.1016/j.heliyon.2024.e30413
Li XKwon JPadmanabhan BZhang P(2023)Introduction to the Special Issue on IT-enabled Business Management and Decision Making in the (Post) Covid-19 EraACM Transactions on Management Information Systems10.1145/362799514:4(1-2)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3627995
Fantini APilato GVitale G(2023)Consistency, Uncertainty or Inconsistency Detection in Multimodal Emotion Recognition2023 Seventh IEEE International Conference on Robotic Computing (IRC)10.1109/IRC59093.2023.00067(377-380)Online publication date: 11-Dec-2023
https://doi.org/10.1109/IRC59093.2023.00067

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