A Critical Analysis of a Tourist Trip Design Problem with Time-Dependent Recommendation Factors and Waiting Times

Porras, Cynthia; Pérez‐Cañedo, Boris; Pelta, David A.; Verdegay, J. L.

doi:10.3390/electronics11030357

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…[10] extended the model by considering that the profit of each node may be a decreasing function of time. Recently, new features have been considered that enrich the optimization models developed; for example, to assume that the benefit derived from the visit to each POI is dependent on the time of day in which it is carried out and, consequently, being able to include the opportunity to modify the departure in the model from a POI to obtain a greater benefit along its exit arc [11,12].…”

Section: Introductionmentioning

confidence: 99%

Designing Ecotourism Routes with Time-Dependent Benefits along Arcs and Waiting Times at Nodes

Piedra-de-la-Cuadra,

Ortega

2024

Mathematics

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Ecotourism routes serve as powerful tools for fostering environmental awareness. To achieve this, it is crucial to design itineraries within natural parks that strike a balance between visitor experience and ecological preservation. Limiting the duration of visits prevents undue strain on both visitors and ecosystems. Effective routes should showcase high biodiversity, traversing diverse sites to enhance knowledge acquisition. Considering natural factors such as light conditions and climate, it is prudent to tailor visiting times to optimize the experience. Therefore, it makes sense to incorporate time-dependent benefits at arcs and the possibility of introducing waiting times at nodes in the design models. These two characteristics have enriched the optimization models developed to solve the tourist trip design problem based on maximizing benefit only when points of interest are visited. However, the specific application of these aforementioned characteristics and enriched optimization models to the arc orientation problem remains yet to be reported on and published in the literature. Our contribution addresses this gap, proposing a route design model with scenic value in the arches of the graph where the benefits perceived by travelers are maximized, taking into account a diversity of evaluations depending on the time of starting the trip through each arc.

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Section: Introductionmentioning

confidence: 99%

Designing Ecotourism Routes with Time-Dependent Benefits along Arcs and Waiting Times at Nodes

Piedra-de-la-Cuadra,

Ortega

2024

Mathematics

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“…Additionally, other personalization-based methodologies [2,8] have been developed to suggest a fantastic visit plan for every traveler, depending on their advantages and inclinations. In the recent trip recommendation model, the time factor was under concentration [9]. One location recommendation objective is to suggest the next POI for the users at an exact time given the users' recorded history of traveling [10].…”

Section: Introductionmentioning

confidence: 99%

A Serendipity-Oriented Personalized Trip Recommendation Model

et al. 2022

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Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.

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“…In [9], re-tried itinerary items in different periods were proposed by combining text-based information and perspective data dispensed from photographs. A few specialists have considered time factors when planning trip proposal models [10]. The outing suggestion has the following problems:…”

Section: Introductionmentioning

confidence: 99%

“…Good fortune comes up if the individual feels surprisingly happy when s/he sees a recommended trip [15,16], i.e., a trip that is not 'appropriate' according to the individual's preferences, and yet, is amazing, even if it was not intentionally searched for by the individual. In the new trip proposal model, the time element is emphasized [10]. One objective is to recommend LOIs at the assigned predetermined times of a user's history of voyaging [17].…”

Section: Introductionmentioning

confidence: 99%

A Fortunate Refining Trip Recommendation Model

Abbas

Amran²,

Abdulraheem³

et al. 2022

Electronics

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Personalized travel recommendations propose locations of interest (LOIs) for users. The LOI sequence suggestion is more complicated than a single LOI recommendation. Only a few studies have considered LOI sequence recommendations. Creating a reliable succession of LOIs is difficult. The two LOIs that follow each other should not be identical or from the same category. It is vital to examine the types of subsequent LOIs when designing a sequence of LOIs. Another issue is that providing precise and accurate location recommendations bores users. It can be tedious and monotonous to look at the same types of LOIs repeatedly. Users may want to change their plans in the middle of a trip. The trip must be dynamic rather than static. To address these concerns in the recommendations, organize a customized journey by looking for continuity, implications, innovation, and surprising (i.e., high levels of amusement) LOIs. We use LOI-likeness and category differences between subsequent LOIs to build sequential LOIs. In our travel recommendations, we leveraged luck and dynamicity. We suggest a fortunate refining trip recommendation (FRTR) to address the issues of identifying and rating user pleasure. An algorithm oof compelling recommendation should offer what we are likely to enjoy and provide spontaneous yet objective components to maintain an open doorway to new worlds and discoveries. In addition, two advanced novel estimations are presented to examine the recommended precision of a sequence of LOIs: regulated precision (RP) and pattern precision (PP). They consider the consistency and order of the LOIs. We tested our strategy using data from a real-world dataset and user journey records from Foursquare dataset. We show that our system outperforms other recommendation algorithms to meet the travel interests of users.

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A Critical Analysis of a Tourist Trip Design Problem with Time-Dependent Recommendation Factors and Waiting Times

Cited by 9 publications

References 26 publications

Designing Ecotourism Routes with Time-Dependent Benefits along Arcs and Waiting Times at Nodes

Designing Ecotourism Routes with Time-Dependent Benefits along Arcs and Waiting Times at Nodes

A Serendipity-Oriented Personalized Trip Recommendation Model

A Fortunate Refining Trip Recommendation Model

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