Giovanni Pilato
Mouzhi Ge
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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.
- [1] 2014. Yelp! Dataset. Retrieved August 30, 2022 from https://www.yelp.com//datasetGoogle Scholar
- [2] 2015. IPIP. Retrieved August 30, 2022 from https://ipip.ori.org/Google Scholar
- [3] 2015. Yelp Categories. Retrieved August 30, 2022 from https://blog.yelp.com/community/yelp/Google Scholar
- [4] 2020. TextBlob tool. Retrieved December 30, 2022 from https://textblob.readthedocs.io/en/dev/Google Scholar
- [5] 2021. Langdetect tool. Retrieved August 30, 2022 from https://pypi.org/project/langdetect/Google Scholar
- [6] 2022. CDC. Retrieved August 30, 2022 from https://www.cdc.gov/coronavirus/2019-ncov/faq.htmlGoogle Scholar
- [7] 2022. NLTK tool. Retrieved December 30, 2022 from https://www.nltk.org/Google Scholar
- [8] 2022. Rivescript. Retrieved August 30, 2022 from https://www.rivescript.com/Google Scholar
- [9] . 2021. Semantic-linked data ontologies for indoor navigation system in response to covid-19. ISPRS Int. J. Geo-Inf. 10, 9 (2021), 607.Google ScholarCross Ref
- [10] . 2015. The safest path via safe zones. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’15). 531–542.Google ScholarCross Ref
- [11] . 2018. Probabilistic fasttext for multi-sense word embeddings. arXiv:1806.02901. Retrieved from https://arxiv.org/abs/1806.02901Google Scholar
- [12] . 2009. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc.Google Scholar
- [13] . 2017. Enriching word vectors with subword information. Trans. Assoc. Comput. Ling. 5 (2017), 135–146.Google ScholarCross Ref
- [14] . 2014. Efficient itinerary planning with category constraints. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 203–212.Google ScholarDigital Library
- [15] . 2008. A cognitive architecture for robot self-consciousness. Artif. Intell. Med. 44, 2 (2008), 147–154.Google ScholarDigital Library
- [16] . 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.Google ScholarCross Ref
- [17] . 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.Google ScholarDigital Library
- [18] . 2020. An innovative user-attentive framework for supporting real-time detection and mining of streaming microblog posts. Soft Comput. 24, 13 (2020), 9663–9682.Google ScholarDigital Library
- [19] . 2022. Sensing technologies for crowd management, adaptation, and information dissemination in public transportation systems: A review. IEEE Sens. J. (2022).Google Scholar
- [20] . 1992. An argument for basic emotions. Cogn. Emot. 6, 3-4 (1992), 169–200.Google ScholarCross Ref
- [21] . 1990. Introduction to knowledge spaces: How to build, test, and search them. Psychol. Rev. 97, 2 (1990), 201.Google ScholarCross Ref
- [22] . 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 .Google ScholarCross Ref - [23] . 2016. Orienteering problem: A survey of recent variants, solution approaches and applications. Eur. J. Operat. Res. 255, 2 (2016), 315–332. Google ScholarCross Ref
- [24] . 2015. An e-commerce website based chatbot. Int. J. Comput. Sci. Inf. Technol. 6, 2 (2015), 1483–1485.Google Scholar
- [25] . 2020. Personalized travel route recommendation from multi-source social media data. Multimedia Tools Appl. 79, 45-46 (2020), 33365–33380.Google ScholarDigital Library
- [26] . 2017. Efficient softmax approximation for GPUs. In International Conference on Machine Learning. PMLR, 1302–1310.Google Scholar
- [27] . 2016. Bag of tricks for efficient text classification. https://arxiv.org/abs/1607.01759Google Scholar
- [28] . 2021. POI recommendation method using LSTM-attention in LBSN considering privacy protection. Complex Intell. Syst. (2021).Google Scholar
- [29] . 2015. Point-of-interest recommender systems: A separate-space perspective. In Proceedings of the IEEE International Conference on Data Mining (ICDM’15), , , , , and (Eds.). 231–240.Google ScholarDigital Library
- [30] . 2019. Tour recommendation and trip planning using location-based social media: A survey. Knowl. Inf. Syst. 60, 3 (2019), 1247–1275.Google ScholarDigital Library
- [31] . 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.Google ScholarCross Ref
- [32] . 2013. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781Google Scholar
- [33] . 2018. Advances in pre-training distributed word representations. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’18).Google Scholar
- [34] . 2020. Sentiment analysis for POI recommender systems. In Proceedings of the 7th International Conference on Information Technology Trends (ITT’20). 174–179.Google ScholarCross Ref
- [35] . 2021. Application-based COVID-19 micro-mobility solution for safe and smart navigation in pandemics. ISPRS Int. J. Geo-Inf. 10, 8 (2021), 571.Google ScholarCross Ref
- [36] . 2021. Improvising personalized travel recommendation system with recency effects. Big Data Min. Anal. 4, 3 (2021), 139–154.Google ScholarCross Ref
- [37] . 2021. Tourist route optimization in the context of Covid-19 pandemic. Sustainability 13, 10 (2021), 5492.Google ScholarCross Ref
- [38] . 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.Google ScholarCross Ref
- [39] . 2020. Improving orienteering-based tourist trip planning with social sensing. Fut. Gener. Comput. Syst. 110 (2020), 931–945.Google ScholarCross Ref
- [40] . 2018. Data-driven social mood analysis through the conceptualization of emotional fingerprints. Proc. Comput. Sci. 123 (2018), 360–365.Google ScholarCross Ref
- [41] . 2022. Social sensing for personalized orienteering mediating the need for sociality and the risk of COVID-19. IEEE Trans. Technol. Soc. (2022), 1–1. Google ScholarCross Ref
- [42] . 2008. A kst-based system for student tutoring. Appl. Artif. Intell. 22, 4 (2008), 283–308.Google ScholarDigital Library
- [43] . 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.Google Scholar
- [44] . 2020. An extensive study on the evolution of context-aware personalized travel recommender systems. Inf. Process. Manag. 57, 1 (2020).Google ScholarDigital Library
- [45] . 2010. Artificial Intelligence a Modern Approach. Pearson Education, Inc.Google Scholar
- [46] . 2017. Finding the most preferred path. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’17). 1–10.Google ScholarDigital Library
- [47] . 2019. Personalized travel recommendation based on sentiment-aware multimodal topic model. IEEE Access 7 (2019), 113043–113052.Google ScholarCross Ref
- [48] . 2020. COVID-19 has changed our lives forever. Financ. Expr. (2020).Google Scholar
- [49] . 2021. Point-of-interest lists and their potential in recommendation systems. Inf. Technol. Tour. 23 (2021), 209–239.Google ScholarCross Ref
- [50] . 2017. Data reliability challenge of cyber-physical systems. In Cyber-Physical Systems, , , , and (Eds.). Academic Press, Boston, 91–101. Google ScholarCross Ref
- [51] . 2015. A new information age. In Social Sensing, , , and (Eds.). Morgan Kaufmann, Boston, 1–11. Google ScholarCross Ref
- [52] . 2020. SeqST-GAN: Seq2Seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Trans. Spatial Algor. Syst. 6, 4 (2020), 1–24.Google ScholarDigital Library
- [53] . 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.Google ScholarDigital Library
- [54] . 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.Google ScholarDigital Library
- [55] . 1996. The five-factor model of personality: Theoretical perspectives. https://www.amazon.de/-/en/Jerry-S-Wiggins/dp/157230068XGoogle Scholar
- [56] . 2018. Personalized POI recommendation based on subway network features and users’ historical behaviors. Wireless Commun. Mobile Comput. 2018 (2018), 1–10.Google ScholarDigital Library
Index Terms
- A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era
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