A survey on mobile tourism Recommender Systems

Gavalas, Damianos; Kasapakis, Vlasios; Konstantopoulos, Charalampos; Mastakas, Konstantinos; Pantziou, Grammati

doi:10.1109/iccitechnology.2013.6579536

Cited by 32 publications

(70 citation statements)

References 24 publications

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“…Well-tuned ILS will yield improved solutions with short computerization. This is in line with the survey by Gavalas et al (2013a) that said Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure and Evolutionary Local Search (GRASP-ELS), Ant Colony Systems (ACS) and Iterative Three Components Heuristics (I3CH) can give satisfactory results for problems in TOPTW. Before those pieces of research, Vansteenwegen et al (2011b) conducted a survey to compare the results of ILS and ACS.…”

Section: Literature Reviewsupporting

confidence: 85%

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

2019

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One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist's itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

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Section: Literature Reviewsupporting

confidence: 85%

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

2019

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“…As an illustration, overwhelmed with the large number and variety of points-of-interest (POIs) in a destination, some tourists will resort to using recommender systems (RSs) to make informed decisions [3]. Various RSs have been developed to suggest POIs, tourist services, usergenerated content and social networking services, routes and tours, and personalised multiple-day tour planning [4]. In order to deliver relevant recommendations, these RSs collect and process sensitive data about users, such as their locations, interests, mobility requirements, previous visits, etc., sometimes without tourists being fully aware of it.…”

Section: Emerging Issuesmentioning

confidence: 99%

Privacy Protection in Tourism: Where We Are and Where We Should Be Heading For

Tussyadiah

Miller

2018

Information and Communication Technologies in Tourism 2019

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The link between information privacy concerns and privacy behaviours has been a focus of extensive investigation in various disciplines. However, little attention has been devoted to this issue in the tourism literature. Spurred by technological development and shaped by tourismrelated environments, emerging privacy issues call for comprehensive yet context-specific studies to ensure tourists are making beneficial privacy choices. This paper first presents a comprehensive review of state-of-the-art research on privacy concerns and behaviours. Then, it suggests a list of overarching research priorities, merging social and technical aspects of privacy protection approaches as they apply to tourism. The priorities include research to measure tourists' privacy concerns, explore specific biases in tourists' privacy decisions, experiment with privacy nudges, and explore how to integrate privacy nudges in system design. Thus, this paper contributes to guiding the direction of future research on privacy protection in tourism.

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“…On the one hand, recommendation systems for tourism have been extensively studied in the literature. Borras et al [3], Gavalas et al [6], and Felfernig et al [5] provide comprehensive surveys on the recommendation of tourism resources. Some systems just harvest public portal information to suggest destinations or to plan trips [13], while others propose the aggregation of tourism-related information in the context of user models for personalisation [7].…”

Section: Related Workmentioning

confidence: 99%

Semantic Profiling and Destination Recommendation based on Crowd-sourced Tourist Reviews

Leal

González–Vélez

Malheiro

et al. 2017

Distributed Computing and Artificial Intelligence, 14th International Conference

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Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the "dreaming stage", proposes the automatic recommendation of personalised destinations based on textual reviews, i.e., a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling-to extract meaningful information from textual reviews-and Semantic Similarityto identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.

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A survey on mobile tourism Recommender Systems

Cited by 32 publications

References 24 publications

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

Privacy Protection in Tourism: Where We Are and Where We Should Be Heading For

Semantic Profiling and Destination Recommendation based on Crowd-sourced Tourist Reviews

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