A model for generating tourist itineraries

Bitonto, Pierpaolo Di; Tria, Francesco Di; Laterza, Maria; Roselli, Teresa; Rossano, Veronica; Tangorra, Filippo

doi:10.1109/isda.2010.5687061

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…The result is a path composed of a chain of events that have a pairwise space-time relationship (e i → e i+1 → e i+2 → … → e n ). A deeper and more detailed discussion of the proposed method is reported in [14].…”

Section: A Methods For Generating Tourist Itinerariesmentioning

confidence: 99%

Automated Generation of Itineraries in Recommender Systems for Tourism

Bitonto

Tria

Laterza

et al. 2010

Current Trends in Web Engineering

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Abstract. Current recommender systems can support tourists in choosing travel products (accommodation, activities, means of transport, etc.), in planning long trips, and in profitably spending time in a specific geographical area such as a region (or a city). In the last case, the system should be able to construct itineraries suited to the tourist's interests. In this paper, a method for generating tourist itineraries in knowledge-based recommender systems is proposed. The method is based on a theoretical model that defines space-time relations among items of intangible cultural heritage (called events) and on transitive closure computation (of the relations), that is able to construct chains of events. The proposed method has been implemented in the T-Path recommender system, that suggests itineraries of cultural events occurring in the Apulia region.

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Section: A Methods For Generating Tourist Itinerariesmentioning

confidence: 99%

Automated Generation of Itineraries in Recommender Systems for Tourism

Bitonto

Tria

Laterza

et al. 2010

Current Trends in Web Engineering

Self Cite

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“…Let η be a real number ∈ [0, 1] representing the weight associated to the individual activity preferences and the public preferences 7 . The NightSplit problem is to find the valid plan plan * that maximizes the following objective function.…”

Section: Definition 6 (Nightsplit)mentioning

confidence: 99%

“…To conclude we would like to mention also the works conducted in [7,17,25] which present recommendation and planning systems targeting a single user only but are interesting for us since they consider models of generating itineraries (for touristic applications) which could be integrated with our tool.…”

Section: Related Workmentioning

confidence: 99%

“…Recommender systems and planners provide tools that can help users to manage these difficulties by filtering information, suggesting solutions, predicting some needs and planning the activities. However, most of the existing tools focus on a single user, so they cannot be used when several users interact and participate in a group activity [17,7]. Tools considering group experiences exist [3,5,20] but they mainly focus on methods for aggregating preferences for a fixed group of users in order to optimize (some notions of) group satisfaction.…”

Section: Introductionmentioning

confidence: 99%

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NightSplitter: A Scheduling Tool to Optimize (Sub)group Activities

Liu

Cosmo

Gabbrielli

et al. 2017

Lecture Notes in Computer Science

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Humans are social animals and usually organize activities in groups. However, they are often willing to split temporarily a bigger group in subgroups to enhance their preferences. In this work we present NightSplitter, an on-line tool that is able to plan movie and dinner activities for a group of users, possibly splitting them in subgroups to optimally satisfy their preferences. We first model and prove that this problem is NP-complete. We then use Constraint Programming (CP) or alternatively Simulated Annealing (SA) to solve it. Empirical results show the feasibility of the approach even for big cities where hundreds of users can select among hundreds of movies and thousand of restaurants.

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“…The data obtained from Apache spark is fed to Natural Language Toolkit (NLTK) [18] for the purpose of data cleansing and pre-processing. The pre-processed data is provided to pre-trained BERT [19] for the purpose of sentiment analysis. The proposed framework converts the results into an elegant userfriendly visual representation.…”

Section: Introductionmentioning

confidence: 99%

Travel and Tourism Management System Using Chatbot Recommender

Mohite

2023

IJSREM

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Chatbots have gained increasing importance for research and practice with a lot of applications available today including Amazon’s Alexa or Apple’s Siri. In this paper, we present the underlying methods and technologies behind a Chatbot for e-tourism that allows people textually communicate with the purpose of booking hotels, planning trips, and asking for interesting sights worth being visit. In particular, we show how model- based reasoning can be used for enhancing user experience during a chat, e.g., in cases where too many possible selections are available or where user preferences are too restricted causing inconsistencies and as a consequence not possible answers to be provided. Besides the underlying foundations, we provide a use case from the intended tourism domain to show how such a model-based chatbot effectively can be used in practice. Key Words: travel recommendation, time sensitivity, recency effect, personalization, social media

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A model for generating tourist itineraries

Cited by 4 publications

References 13 publications

Automated Generation of Itineraries in Recommender Systems for Tourism

Automated Generation of Itineraries in Recommender Systems for Tourism

NightSplitter: A Scheduling Tool to Optimize (Sub)group Activities

Travel and Tourism Management System Using Chatbot Recommender

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