Tourism Recommendation Using Social Media Profiles

Kavitha, S.; Vijay, J.; Rajeswari, S.

doi:10.1007/978-981-10-3174-8_22

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Recent studies focus on tourist reviews to build user profiles. Kavitha et al [23] exploit TripAdvisor reviews and social media profiles metadata to build a destination recommendation engine based on users' previously visited locations and matching user experiences about a destination. Moreover, Leal et al [17] designed an algorithm for personalized destinations recommendation based on Expedia reviews by applying content-based filtering to topic-modeled tourists and locations.…”

Section: User Profiling In Tourism-related Platformsmentioning

confidence: 99%

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Yfantidou,

Karanatsiou

et al. 2024

Preprint

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This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

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Section: User Profiling In Tourism-related Platformsmentioning

confidence: 99%

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Yfantidou,

Karanatsiou

et al. 2024

Preprint

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show abstract

“…Recent studies have focused on tourist reviews to build user profiles. Kavitha et al [29] exploit TripAdvisor reviews and social media profiles metadata to build a destination recommendation engine based on users' previously visited locations and matching user experiences about a destination. Moreover, Leal et al [23] designed an algorithm for personalized destinations recommendation based on Expedia reviews by applying content-based filtering to topic-modeled tourists and locations.…”

Section: User Profiling In Tourism-related Platformsmentioning

confidence: 99%

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Yfantidou,

Karanatsiou

et al. 2024

Tourism and Hospitality

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Fishing tourism, a niche yet burgeoning sector within the broader tourism industry, presents a unique opportunity to explore the dynamics of the Experience Economy. This study delves into the intricacies of fishing tourism through the lens of data science, leveraging web crawling techniques to gather extensive data from reviewer profiles on TripAdvisor. By employing natural language processing (NLP) techniques, the study investigates the correlation between Experience Economy dimensions and user profiling aspects within this domain. The findings of this study shed light on the predominant dimensions of the Experience Economy within fishing tourism. “Entertainment” emerges as the primary dimension, closely followed by “Aesthetic”, “Educational”, and “Escapist” elements. Notably, the study reveals frequent co-occurrences of certain dimension pairs, such as “Entertainment”–“Aesthetic” and “Educational”–“Entertainment”, underscoring the multifaceted nature of the fishing tourism experience. The implications of this research extend beyond academic discourse to practical considerations for stakeholders in the fishing tourism sector. By highlighting the socioeconomic benefits inherent in fishing tourism for local communities and fishers, the study emphasizes the importance of governmental support. This support, in the form of infrastructure development, effective leadership, legislative measures, and financial backing, is deemed essential for fostering sustainable growth and development in this sector. This study stands as a pioneering endeavor within the realm of fishing tourism research, particularly in its focus on Experience Economy dimensions and user profiling. By drawing data from both business pages and user profiles on TripAdvisor, it provides a comprehensive understanding of the intricate interplay between tourist experiences and the broader economic and social landscape of fishing tourism destinations.

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“…Sequence of tagged location travelled and visited by the user is extracted to identify the travel patterns of the user. The tourist destinations extracted are generally portrayed in the form of either sequences or tree structure as given in Figure 1 to identify the travel patterns and dependability between the extracted destinations (Kavitha et al, 2017).…”

Section: Tokenization and Pos Taggingmentioning

confidence: 99%

A User Preference Tree based Personalized Route Recommendation System for Constraint Tourism and Travel

Kanimozhi¹,

Ganapathy

Manjula

et al. 2021

Preprint

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Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses User-Location Vector with LDA and Jaccard Coefficients. Moreover, a new Tourist Destination tree is constructed using the posts extracted from TripAdvisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based Travelling Salesman Problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best Tourist Destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed User Centric Personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.

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Tourism Recommendation Using Social Media Profiles

Cited by 11 publications

References 11 publications

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

A User Preference Tree based Personalized Route Recommendation System for Constraint Tourism and Travel

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