Personalized Attraction Recommendation System for Tourists Through Check-In Data

Kesorn, Kraisak; Juraphanthong, Wanarat; Salaiwarakul, Anongporn

doi:10.1109/access.2017.2778293

Cited by 55 publications

(28 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for Tourists through Check-In Data K, Kesorn, W. Juraphanthong, and A. Salaiwarakul from Computer Science and Information Technology Department, Naresuan University, Phitsanulok, Thailand publish Personalized Attraction Recommendation System [16]. This paper demonstrates the usefulness of the data available on Facebook through the example studies involving attraction recommendations, resolving the cold-start problem, and adapting the user model to improve recommendation quality in the tourism domain.…”

Section: F Personalized Attraction Recommendation Systemmentioning

confidence: 85%

Data Science Recommendation System using Semantic Technology

Shah,

2019

IJEAT

View full text Add to dashboard Cite

Data Science is a field in which multidisciplinary blend of data inference, algorithm development, and technology are combined in order to solve analytically complex problems. Data science field tend to focus on a complex and large algorithmic technological problem having data at the core. Students at the starting of the learning phase don’t know all the technological and algorithmic aspects related to data science. Consulting through faculty or knowing from external source helps them to proceed towards the needed expertise that they want to gain. Data science recommendation system using semantic web data-science ontology and service-oriented architecture is proposed in our work to recommend students the appropriate resources for their queries. Recommending student significant information concerning books, on-line documentation, software tools, public code repositories, and experts, tutors to be contacted based on student query or previous exam scores is the main objective of the work. Ontology based service-oriented web platform with a conversational user interface that use NLP (Natural Language Processing) to recommend possible resources is proposed and tested to be efficient

show abstract

Section: F Personalized Attraction Recommendation Systemmentioning

confidence: 85%

Data Science Recommendation System using Semantic Technology

Shah,

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Depending on the POIs that the user has visited, it is also possible to deduce which group of users she/he belongs to. In [15], for example, the user profile is characterised by her/his name, office location, and social networks (e.g., friends on Facebook), and her/his preferences are deduced from POIs that he/she, or his/her friends, have visited and shared on social networks (e.g., Facebook). The approaches proposed in [16,17] consider only the preferences of the user.…”

Section: Related Workmentioning

confidence: 99%

“…Different forms of combination have been considered [25]: (i) separate implementation of content-based and collaborative filtering approaches and then the combination of recommendation results, (ii) integration of some content-based treatments into a collaborative filtering approach, (iii) integrating some collaborative filtering-based treatments into a content-based approach, (iv) proposing a general approach that integrates both content-based and collaborative filtering approaches. In the first category, we can mention the approaches of [15,16,26], which are differentiated by the techniques employed for content processing and collaborative filtering. Refs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Rajaonarivo

Fonteles

Sallaberry

et al. 2019

IJGI

View full text Add to dashboard Cite

The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.

show abstract

“…Wozniak et al analyzed 150 DMOs' social media returns of investment in Belgium, France, and Switzerland, and they used the Fanpage Karma web application for FB pages' data extraction as outcome variables [12]. Kesorn et al, within their PTIS framework, used the Facebook Graph API extracts covering UGC on check-in data at points of interest (POI) and their feedback for personalized recommendations on visiting POIs in destinations [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?

et al. 2019

View full text Add to dashboard Cite

The paper follows-up ongoing research focusing on the potential of machine-readable data as additional knowledge in the governance of local tourism and destination management organizations (DMOs) in Slovakia. The current focus is on one classic social media (Facebook), one location-based social media (Foursquare), two hybrid travel-related platforms with partial attributes of reservation services (Google Places, TripAdvisor), and two online reservation services (Booking, Airbnb). The global aim is the usage of extracted data for the identification of additional entities with the obligation of local occupancy taxation, which is the financial backbone of Slovak (DMOs). A set of simple and globally reusable scripts constructed in Python and PostgreSQL were used to extract data on lodging providers from the Google Places application programming interface (API), the Facebook Place Search API and the Foursquare Venue API over grid overlays of districts' spatial representation. For pure scientific purposes in the case of Tripadvisor, Booking, and Airbnb, with no suitable access to open APIs, web scraping methods were used for data extraction. The pilot case was applied in the boundaries of Kosice city (Slovakia), and the aggregations of processed data were compared with official open statistics. Results indicate that the automated continuous monitoring of online platforms could help local public administrations in decreasing occupancy tax evasions and even widen knowledge about online audiences and visitors' satisfaction.

show abstract

Personalized Attraction Recommendation System for Tourists Through Check-In Data

Cited by 55 publications

References 59 publications

Data Science Recommendation System using Semantic Technology

Data Science Recommendation System using Semantic Technology

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?

Contact Info

Product

Resources

About