research-article

Serendipity-based Points-of-Interest Navigation

Authors:

Panos K. Chrysanthis,

Konstantinos Pelechrinis,

Demetrios Zeinalipour-Yazti, and

Mohamed A. SharafAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 4

Article No.: 33, Pages 1 - 32

https://doi.org/10.1145/3391197

Published: 01 October 2020 Publication History

Abstract

Traditional venue and tour recommendation systems do not necessarily provide a diverse set of recommendations and leave little room for serendipity. In this article, we design MPG, a Mobile Personal Guide that recommends: (i) a set of diverse yet surprisingly interesting venues that are aligned to user preferences and (ii) a set of routes, constructed from the recommended venues. We also introduce EPUI, an Experimental Platform for Urban Informatics. Our comparison with the state-of-the-art schemes indicates that MPG is capable of providing high-quality venues and route recommendations while incorporating seamlessly both the notion of diversity and that of serendipity.

References

[1]

2015. Foursquare Category Hierarchy. Retrieved from https://developer.foursquare.com/categorytree.

[2]

2019. The Nationality Rooms. Retrieved from https://en.wikipedia.org/wiki/Nationality_Rooms.

[3]

Making P. Adamopoulos. 2016. Unexpectedness and Non-Obviousness in Recommendation Technologies and Their Impact on Consumer Decision. Ph.D. Dissertation. New York University.

[4]

Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying search results. In Proceedings of the ACM International Conference on Web Search and Data Mining. 5--14.

Digital Library

[5]

Liliana Ardissono, Anna Goy, Giovanna Petrone, Marino Segnan, and Pietro Torasso. 2003. Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices. Appl. Artif. Intell. 17, 8--9 (2003).

[6]

S. Basu Roy, G. Das, S. Amer-Yahia, and C. Yu. 2011. Interactive itinerary planning. In Proceedings of the 2011 IEEE 27th International Conference on Data Engineering. 15--26.

[7]

I. Benouaret and D. Lenne. 2016. A composite recommendation system for planning tourist visits. In Proceedings of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence. 626--631.

[8]

Betim Berjani and Thorsten Strufe. 2011. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems. Article 4, 6 pages.

Digital Library

[9]

Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 335--336.

Digital Library

[10]

L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi. 2018. Ranking with fairness constraints. In Proceedings of the 45th International Colloquium on Automata, Languages, and Programming. 28:1--28:15.

[11]

Zaiben Chen, Heng Tao Shen, Xiaofang Zhou, Yu Zheng, and Xing Xie. 2010. Searching trajectories by locations: An efficiency study. In Proceedings of the ACM Special Interest Group on Management of Data Conference. 255--266.

Digital Library

[12]

Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. 2011. Exploring millions of footprints in location sharing services. In Proceedings of the International AAAI Conference on Web and Social Media.

[13]

Paolo Ciaccia, Marco Patella, and Pavel Zezula. 1997. M-tree: An efficient access method for similarity search in metric spaces. In Proceedings of the International Conference on Very Large Data Bases. 426--435.

[14]

Paolo Ciaccia, Marco Patella, and Pavel Zezula. 1998. A cost model for similarity queries in metric spaces. In Proceedings of the Symposium on Principles of Database Systems. 59--68.

Digital Library

[15]

Charles L. A. Clarke, Maheedhar Kolla, Gordon V. Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008. Novelty and diversity in information retrieval evaluation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 659--666.

Digital Library

[16]

Munmun De Choudhury, Moran Feldman, Sihem Amer-Yahia, Nadav Golbandi, Ronny Lempel, and Cong Yu. 2010. Automatic construction of travel itineraries using social breadcrumbs. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. 35--44.

Digital Library

[17]

Marina Drosou and Evaggelia Pitoura. 2012. Dynamic diversification of continuous data. In Proceedings of the International Conference on Extending Database Technology. 216--227.

Digital Library

[18]

Marina Drosou and Evaggelia Pitoura. 2012. Result diversification based on dissimilarity and coverage. In Proceedings of the International Conference on Very Large Data Bases. 13--24.

[19]

Marina Drosou and Evaggelia Pitoura. 2015. Multiple Radii DisC diversity: Result diversification based on dissimilarity and coverage. ACM Trans. Datab. Syst. 40, 1 (2015).

[20]

Simon Dunstall, Mark E. T. Horn, Philip Kilby, Mohan Krishnamoorthy, Bowie Owens, David Sier, and Sylvie Thiébaux. 2003. An automated itinerary planning system for holiday travel. J. IT Tour. 6, 3, 195--210.

[21]

Esther Galbrun, Konstantinos Pelechrinis, and Evimaria Terzi. 2016. Urban navigation beyond shortest route: The case of safe paths. Inf. Syst. 57 (2016), 160--171.

Digital Library

[22]

Xiaoyu Ge and Panos K. Chrysanthis. 2020. PrefDiv: Efficient algorithms for effective top-k result diversification. In Proceedings of the International Conference on Extending Database Technology. 335--346.

[23]

Xiaoyu Ge, Panos K. Chrysanthis, and Alexandros Labrinidis. 2015. Preferential diversity. In Proceedings of the 2nd International Workshop on Exploratory Search in Databases and the Web. 9--14.

Digital Library

[24]

Xiaoyu Ge, Panos K. Chrysanthis, and Konstantinos Pelechrinis. 2016. MPG: Not so random exploration of a city. In Proceedings of the IEEE 17th International Conference on Mobile Data Management. 72--81.

[25]

Xiaoyu Ge, Panos K. Chrysanthis, Konstantinos Pelechrinis, and Demetrios Zeinalipour-Yazti. 2018. EPUI: Experimental platform for urban informatics. In Proceedings of the ACM Special Interest Group on Management of Data Conference. 1761--1764.

Digital Library

[26]

Xiaoyu Ge, Ameya Daphalapurkar, Manali Shimpi, Darpun Kohli, Konstantinos Pelechrinis, Panos K. Chrysanthis, and Demetrios Zeinalipour-Yazti. 2017. Data-driven serendipity navigation in urban places. In Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems. 2501--2504.

[27]

Roxana Gheorghiu, Alexandros Labrinidis, and Panos K. Chrysanthis. 2014. A user-friendly framework for database preferences. In Proceedings of the IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. 205--214.

[28]

Aristides Gionis, Theodoros Lappas, Konstantinos Pelechrinis, and Evimaria Terzi. 2014. Customized tour recommendations in urban areas. In Proceedings of the ACM International Conference on Web Search and Data Mining. 313--322.

Digital Library

[29]

Jean-Benoit Griesner, Talel Abdessalem, and Hubert Naacke. 2015. POI recommendation: Towards fused matrix factorization with geographical and temporal influences. In Proceedings of the ACM Recommender Systems Conference. 301--304.

Digital Library

[30]

Zaeem Hussain, Hina A. Khan, and Mohamed A. Sharaf. 2015. Diversifying with few regrets, but too few to mention. In Proceedings of the International Workshop on Exploratory Search in Databases and the Web. 27--32.

[31]

Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7, 1 (2016), 2:1--2:42.

Digital Library

[32]

Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the ACM Recommender Systems Conference. 79--96.

Digital Library

[33]

Hina A. Khan, Marina Drosou, and Mohamed A. Sharaf. 2013. Scalable diversification of multiple search results. In Proceedings of the ACM Conference on Information and Knowledge Management. 775--780.

[34]

Hina A. Khan and Mohamed A. Sharaf. 2015. Progressive diversification for column-based data exploration platforms. In Proceedings of the IEEE International Conference on Data Engineering. 327--338.

[35]

Hina A. Khan and Mohamed A. Sharaf. 2017. Model-based diversification for sequential exploratory queries. Data Sci. Eng. 2, 2 (2017), 151--168.

[36]

Hina A. Khan, Mohamed A. Sharaf, and Abdullah Albarrak. 2014. DivIDE: Efficient diversification for interactive data exploration. In Proceedings of the International Conference on Scientific and Statistical Database Management. 15:1--15:12.

Digital Library

[37]

Werner Kiessling and Gerhard Kostler. 2002. Preference SQL: Design, implementation, experiences. In Proceedings of the International Conference on Very Large Data Bases. 990--1001.

[38]

Jinyoung Kim, Hyungjin Kim, and Jung-hee Ryu. 2009. TripTip: A trip planning service with tag-based recommendation. In CHI’09 Extended Abstracts on Human Factors in Computing Systems. 3467--3472.

[39]

Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. 2018. Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the ACM Symposium On Applied Computing. 1341--1350.

Digital Library

[40]

Takeshi Kurashima, Tomoharu Iwata, Go Irie, and Ko Fujimura. 2010. Travel route recommendation using geotags in photo sharing sites. In Proceedings of the ACM Conference on Information and Knowledge Management. 579--588.

Digital Library

[41]

D. M. Levinson. 2016. A random walk down main street. Journal of Land Use, Mobility and Environment 9, 2 (2016), 31--40.

[42]

Defu Lian, Cong Zhao, Guangzhong Sun Xing Xie, Enhong Chen, and Yong Rui. 2014. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining. 831--840.

Digital Library

[43]

Qiuxia Lu, Tianqi Chen, Weinan Zhang, Diyi Yang, and Yong Yu. 2012. Serendipitous personalized ranking for top-N recommendation. In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. 258--265.

[44]

Ziyu Lu, Hao Wang, Nikos Mamoulis, Wenting Tu, and David W. Cheung. 2017. Personalized location recommendation by aggregating multiple recommenders in diversity. Geoinformatica 21, 3 (2017), 459--484.

Digital Library

[45]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the Conference on Neural Information Processing Systems. 3111--3119.

[46]

Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama, Ko Fujimura, and Toru Ishida. 2010. Classical music for rock fans?: Novel recommendations for expanding user interests. In Proceedings of the ACM Conference on Information and Knowledge Management. 949--958.

Digital Library

[47]

Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. 2012. A random walk around the city: New venue recommendation in location-based social networks. In Proceedings of the ASE/IEEE International Conference on Social Computing and ASE/IEEE International Conference on Privacy, Security, Risk, and Trust. 144--153.

Digital Library

[48]

Debmalya Panigrahi, Atish Das Sarma, Gagan Aggarwal, and Andrew Tomkins. 2012. Online selection of diverse results. In Proceedings of the 5th International Conference on Web Search and Web Data Mining. 263--272.

Digital Library

[49]

Hae-Sang Park and Chi-Hyuck Jun. 2009. A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36, 2 (2009), 3336--3341.

Digital Library

[50]

Lu Qin, Jeffrey Xu Yu, and Lijun Chang. 2012. Diversifying top-K results. In Proceedings of the International Conference on Very Large Data Bases. 1124--1135.

Digital Library

[51]

Daniele Quercia, Rossano Schifanella, and Luca Maria Aiello. 2014. The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In Proceedings of the 25th ACM Conference on Hypertext and Social Media. ACM, 116--125.

Digital Library

[52]

Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). 926--934.

[53]

Kostas Stefanidis, Georgia Koutrika, and Evaggelia Pitoura. 2011. A survey on representation, composition and application of preferences in database systems. ACM Trans. Datab. Syst. 36, 19 (2011).

[54]

Hanghang Tong, Jingrui He, Zhen Wen, Ravi Konuru, and Ching-Yung Lin. 2011. Diversified ranking on large graphs: An optimization viewpoint. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining. 1028--1036.

Digital Library

[55]

Ling-Yin Wei, Yu Zheng, and Wen-Chih Peng. 2012. Constructing popular routes from uncertain trajectories. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining. 195--203.

Digital Library

[56]

Hongfei Xu, Yu Gu, Jianzhong Qi, Jiayuan He, and Ge Yu. 2019. Diversifying top-k routes with spatial constraints. J. Comput. Sci. Technol. 34, 4 (2019), 818--838.

[57]

Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of the International Conference on Advances in Geographic Information Systems. 458--461.

Digital Library

[58]

Hyoseok Yoon, Yu Zheng, Xing Xie, and Woontack Woo. 2012. Social itinerary recommendation from user-generated digital trails. Pers. Ubiq. Comput. 16, 5 (2012), 469--484.

Digital Library

[59]

Cong Yu, Laks Lakshmanan, and Sihem Amer-Yahia. 2009. It takes variety to make a world: Diversification in recommender systems. In Proceedings of the International Conference on Extending Database Technology. 368--378.

Digital Library

[60]

Chenyi Zhang, Hongwei Liang, and Ke Wang. 2016. Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Trans. Inf. Syst. 35, 1 (September 2016), 5:1--5:28.

Digital Library

[61]

Zhu Zhang, Daniel Zeng, Ahmed Abbasi, Jing Peng, and Xiaolong Zheng. 2013. A random walk model for item recommendation in social tagging systems. ACM Trans. Manage. Inf. Syst. 4, 8 (2013).

Digital Library

[62]

Xiaofei Zhu, Jiafeng Guo, Xueqi Cheng, Pan Du, and Huawei Shen. 2011. A unified framework for recommending diverse and relevant queries. In Proceedings of the Web Conference. 37--46.

Digital Library

[63]

Cai-Nicolas Ziegler, Joseph A. Konstan Sean M. McNee, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the Web Conference. 22--32.

Digital Library

Cited By

Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Ziarani RRavanmehr R(2021)Deep Neural Network Approach for a Serendipity-Oriented Recommendation SystemExpert Systems with Applications10.1016/j.eswa.2021.115660(115660)Online publication date: Jul-2021
https://doi.org/10.1016/j.eswa.2021.115660

Index Terms

Serendipity-based Points-of-Interest Navigation
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity
      2. Top-k retrieval in databases
  2. Information systems applications
    1. Mobile information processing systems

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 4

November 2020

391 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3427795

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 October 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 February 2020

Received: 01 May 2019

Published in TOIT Volume 20, Issue 4

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Cyprus RPF EnterCY (INTEGRATED/0916/0020)
Alexander von Humboldt-Foundation, Germany
U.S. National Science Foundation
National Institutes of Health
UAE University

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Cited By

Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Ziarani RRavanmehr R(2021)Deep Neural Network Approach for a Serendipity-Oriented Recommendation SystemExpert Systems with Applications10.1016/j.eswa.2021.115660(115660)Online publication date: Jul-2021
https://doi.org/10.1016/j.eswa.2021.115660

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