Visit Planner: A Personalized Mobile Trip Design Application based on a Hybrid Recommendation Model

Author:

Papadakis Harris1,Panagiotakis Costas1,Fragopoulou Paraskevi1,Chalkiadakis Georgios2,Streviniotis Errikos2,Ziogas Ioannis-Panagiotis2,Koutsmanis Michail2,Bariamis Panagiotis3

Affiliation:

1. Hellenic Mediterranean University

2. Technical University of Crete

3. Netmechanics LLC

Abstract

Abstract The paper presents Visit Planner (ViP), a mobile application prototype that provides a solution to the challenging tourist trip design problem. ViP follows a holistic approach offering personalized recommendations for Points of Interest (POIs) based on preferences either explicitly collected by the application, or inferred by the users’ ongoing interaction with the system. ViP proposes to the final user, a trajectory of POIs calculated using an Expectation Maximization method that maximizes user satisfaction taking into consideration a variety of time and spatial constraints for both users and POIs. Additionally, POIs are divided into categories, so that a certain number of POIs from each category to be included in the final itinerary. The application is implemented as a user-interactive system that allows the flexibility for easy content adaptation and facilitates management of content and services by the user.The prototype has been implemented for Android-based smartphones, on an open application environment, using standard communication protocols and open database technology. Currently, it is applied to the city of Agios Nikolaos in Crete, and is available for download from Google play. MSC Classification: 68T20 , 68N99

Publisher

Research Square Platform LLC

Reference58 articles.

1. Nicola Barbieri and Gianni Costa and Giuseppe Manco and Riccardo Ortale (2011) Modeling item selection and relevance for accurate recommendations: a bayesian approach. {ACM}, 21--28, 2011 {ACM} Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, October 23-27, 2011, Bamshad Mobasher and Robin D. Burke and Dietmar Jannach and Gediminas Adomavicius

2. Evangelos Tripolitakis and Georgios Chalkiadakis (2016) Probabilistic Topic Modeling, Reinforcement Learning, and Crowdsourcing for Personalized Recommendations. Springer, 157--171, 10207, Lecture Notes in Computer Science, Multi-Agent Systems and Agreement Technologies - 14th European Conference, {EUMAS} 2016, and 4th International Conference, {AT} 2016, Valencia, Spain, December 15-16, 2016, Revised Selected Papers, Natalia Criado Pacheco and Carlos Carrascosa and Nardine Osman and Vicente Juli{\'{a}}n Inglada

3. Nielsen, Frank and Nock, Richard (2009) Emerging Trends in Visual Computing: LIX Fall Colloquium, ETVC 2008, Palaiseau, France, November 18-20, 2008. Revised Invited Papers. Springer Berlin Heidelberg, Berlin, Heidelberg, 978-3-642-00826-9, 164--174

4. Panagiotakis, Costas and Papadakis, Harris and Papagrigoriou, Antonis and Fragopoulou, Paraskevi (2021) Improving recommender systems via a Dual Training Error based Correction approach. Expert Systems with Applications 183: 115386 Elsevier

5. Chen, Lei and Cao, Jie and Chen, Huanhuan and Liang, Weichao and Tao, Haicheng and Zhu, Guixiang (2021) {Attentive multi-task learning for group itinerary recommendation}. Knowledge and Information Systems 63(7): 1687--1716 Springer London

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