<i>e-TOURISM</i>: A TOURIST RECOMMENDATION AND PLANNING APPLICATION

Sebastiá, Laura; García, Inma Mendoza; Onaindía, Eva; Guzmán, C.

doi:10.1142/s0218213009000378

Cited by 90 publications

(51 citation statements)

References 4 publications

(7 reference statements)

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“…Specifically, we have used a Tourism data set, which was developed in our research group [36]. It contains information about leisure and tourist activities in the city of Valencia (Spain).…”

Section: Resultsmentioning

confidence: 99%

A negotiation framework for heterogeneous group recommendation

García

Sebastiá

2014

Expert Systems with Applications

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Over the last years, some remarkable recommender systems for group of users have been developed. When using most of these systems, each group member communicates his/her preferences to the system, which obtains a group profile as the result of an equal weighting of the individual preferences. This way, no member is particularly dissatisfied with the recommendations. However, this is not a realistic situation, given that not all the members in a group act in the same manner. This paper deals with the problem of recommendation for a group of users, where, besides his/her own preferences, each user may have different expectations about the result of the recommendation and may exhibit a different behaviour with respect to the other group members. Moreover, all this information is private and may be revealed under certain circumstances. In this context, we have opted for building a multi-agent system, where an agent acts on behalf of one group member. We have implemented a UserAgent that can be configured in order to exhibit the behaviour desired by the corresponding user. Then, different UserAgents negotiate with the aim of building a group profile that satisfies their particular minimum requirements, while preserving some privacy. Moreover, we have designed a NegotiatorAgent, which governs the negotiation and may act as a mediator in order to facilitate the agreement. Finally, we have performed some experiments that show that this mechanism is able to give a response in this heterogeneous environment.

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Section: Resultsmentioning

confidence: 99%

A negotiation framework for heterogeneous group recommendation

García

Sebastiá

2014

Expert Systems with Applications

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“…With the presence of the crowd to recommend POIs, the search space to find a feasible solution to the planning problem can be reduced. In solving the trip planning problem, CYTRIP is similar to other systems (Sebastia et al, 2009), that is, formulating the problem as an Artificial Intelligence (AI) planning problem in PDDL3 (Gerevini and Long, 2005) and using an existing planner to solve it. However, the work by those researchers is only applicable for a single-day trip.…”

Section: Related Workmentioning

confidence: 99%

Multi-day Trip Planning System with Collaborative Recommendation

Aprilia¹,

Oh²,

Hong³

et al. 2016

Journal of Intelligence and Information Systems

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․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 Priska ApriliaㆍKyeong-Jin OhㆍMyung-Duk HongㆍMyeong-Hyeon GaㆍGeun-Sik Jo 160 ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources av...

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“…e-Tourism (Sebastia et al, 2008) is a tourist recommendation and planning application to assist users on the organization of a tourism agenda in the city of Valencia (Spain). This project focuses essentially on the trip planning and, besides activities duration, it introduces a new feature which is the use of different visit durations for each point of interest based on the different user's profiles.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile application to provide personalized sightseeing tours

Anacleto¹,

Figueiredo²,

Almeida³

et al. 2014

Journal of Network and Computer Applications

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a b s t r a c tTourist recommendation systems have been growing over the last few years, mainly because of the use of mobile devices to obtain user context. This work discusses some of the most relevant systems on the field and presents PSiS Mobile, which is a mobile recommendation and planning application designed to support a tourist during his vacations. It provides recommendations about points of interest to visit based on tourist preferences and on user and sight context. Also, it suggests a visit planning which can be dynamically adapted based on current user and sight context. This tool works also like a journey dairy since it records the tourist moves and tasks to help him remember how the trip was like. To conclude, some field experiences will be presented.

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e-TOURISM: A TOURIST RECOMMENDATION AND PLANNING APPLICATION

Cited by 90 publications

References 4 publications

A negotiation framework for heterogeneous group recommendation

A negotiation framework for heterogeneous group recommendation

Multi-day Trip Planning System with Collaborative Recommendation

Mobile application to provide personalized sightseeing tours

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