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Applied Sciences
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State of the Art in Recommendation and Mobile Systems for...
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State of the Art in Recommendation and Mobile Systems for Tourism

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3661

Special Issue Editors

Prof. Dr. Tsvi Kuflik

E-Mail Website
Guest Editor
Department of Information Systems, University of Haifa, Haifa 3498838, Israel
Interests: digital humanities; advanced technology for cultural heritage; user modeling; intelligent user interfaces; smart environments; artificial intelligence; text mining; cultural heritage; machine learning; data mining and knowledge discovery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fabiana Vernero

E-Mail Website
Guest Editor
Department of Computer Science, University of Turin, C.so Svizzera 185, 10149 Turin, Italy
Interests: intelligent user interfaces; recommender systems

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue entitled “State of the Art in Recommendation and Mobile Systems for Tourism”.

The tourism industry is facing a major challenge given the COVID-19 pandemic. At first, traveling stopped, then started again with ups and downs, and tourism is still struggling as we are learning to live with the pandemic. However, while the tourism industry is changing, technology support for tourism is expanding its reach. In addition to classical tourist guides, virtual tourism gained attention. As we travel less, we still want to experience remote sites.

Prof. Dr. Tsvi Kuflik
Prof. Dr. Fabiana Vernero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mobile tourist guides
  • mobile visitors guides
  • mobile museum guides
  • smart museums
  • next PoI prediction
  • tourist recommender systems
  • tourist recommenders for groups
  • package and sequence tourist recommendations
  • temporal constraints in tourist recommenders
  • personality-based tourist recommenders
  • context-aware tourist recommenders

Published Papers (2 papers)

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Research

20 pages, 4865 KiB  
Article
Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice
by Reham Alabduljabbar
Appl. Sci. 2023, 13(17), 9574; https://doi.org/10.3390/app13179574 - 24 Aug 2023
Cited by 3 | Viewed by 1631
Abstract
Saudi Arabia’s tourism sector has recently started to play a significant role as an economic driver. The restaurant industry in Riyadh has experienced rapid growth in recent years, making it increasingly challenging for customers to choose from the large number of restaurants available. [...] Read more.
Saudi Arabia’s tourism sector has recently started to play a significant role as an economic driver. The restaurant industry in Riyadh has experienced rapid growth in recent years, making it increasingly challenging for customers to choose from the large number of restaurants available. This paper proposes a matrix factorization collaborative-based recommender system for Riyadh city restaurants. The system leverages user reviews and ratings to predict users’ preferences and recommend restaurants likely to be of interest to them. The system incorporates three different approaches, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++). To the best of our knowledge, this is the first recommender system specifically designed for Riyadh restaurants. A comprehensive dataset of restaurants in Riyadh was collected, scraped from Foursquare.com, which includes a wide range of restaurant features and attributes. The dataset is publicly available, enabling other researchers to replicate the experiments and build upon the work. The performance of the system was evaluated using a real-world dataset, and its effectiveness was demonstrated by comparing it to a state-of-the-art recommender system. The evaluation results showed that SVD and NMF are effective methods for generating recommendations, with SVD performing slightly better in terms of RMSE and NMF performing slightly better in terms of MAE. Overall, the findings suggest that the collaborative-based approach using matrix factorization algorithms is an effective way to capture the complex relationships between users and restaurants. Full article
(This article belongs to the Special Issue State of the Art in Recommendation and Mobile Systems for Tourism)
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28 pages, 28842 KiB  
Article
Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data
by Yue Hu, Zhixiang Fang, Xinyan Zou, Haoyu Zhong and Lubin Wang
Appl. Sci. 2023, 13(1), 596; https://doi.org/10.3390/app13010596 - 01 Jan 2023
Cited by 3 | Viewed by 1351
Abstract
Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with [...] Read more.
Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with crowd dynamics (TTDP-CD), which is quantified with the crowd dynamics indicators derived from mobile tracking data in terms of crowd flow, crowd interaction, and crowd structure. TTDP-CD attempts to minimize the perceived crowding and maximize the assessed value of destinations while minimizing the total distance and proposes a two-stage route strategy of “global optimization first, local update later” to deal with the sudden increase in crowding in realistic scenarios. An evolutionary algorithm is extended with container-index coding, mixed mutation operators, and a global archive to create a personalized day tour route at the urban scale. To corroborate the performance of this approach, a case study was carried out in Dalian, China. The results demonstrate that the suggested method outperforms previous approaches, such as NSGA-II, MOPSO, MOACO, and WSM, in terms of performance and solution quality and decreases real-time crowding by an average of 7%. Full article
(This article belongs to the Special Issue State of the Art in Recommendation and Mobile Systems for Tourism)
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