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Serendipity-based Points-of-Interest Navigation

Authors:

Panos K. Chrysanthis,

Konstantinos Pelechrinis,

Demetrios Zeinalipour-Yazti,

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.

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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

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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