Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Abstract

Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

Information accessibility as an important global factor in accessible tourism

“…The use of presentation or promotion through websites is a matter of course for entities active in the field of tourism. Therefore, more and more emphasis is placed on the content, updating of information, creation of product databases with the possibility of their reservation, flexibility and effectiveness of these supportive means of promotion or easy orientation in the structure of the site (Yochum et al, 2020).…”

Recommendation agents and information sharing through social media for coronavirus outbreak

“…Recently, numerous studies conducted on tourism recommendation systems ( Jeong et al, 2020a , Yochum et al, 2020 ) and have discussed research challenges and a general overview of this system. For instance, Bahramian and Abbaspour (2015) conducted research on the tourism recommender system and developed a new recommender system to overcome information overload difficulty.…”

Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation

“…A user with insufficient preference and ratings in one domain have sufficient ratings in similar or other domains. The process of leveraging these sufficient ratings to generate better and accurate recommendations is known as cross-domain recommendation [4,5]. The transfer of knowledge across different domains to improve recommendation results is the main idea of cross-domain recommender systems [6,7].…”