Academic journal article European Journal of Tourism Research

Social Trust Based Semantic Tourism Recommender System: A Case of Medical Tourism in Tunisia

Academic journal article European Journal of Tourism Research

Social Trust Based Semantic Tourism Recommender System: A Case of Medical Tourism in Tunisia

Article excerpt

(ProQuest: ... denotes formulae omitted.)


Personalized recommender systems are now playing an important role in providing better tourism experiences to many tourists. The major objective behind applying personalized recommender system to medical tourism information services is to allow tourists to have efficient information searching experiences and to boost the individuation of tourism information. The effect social networks have on product marketing has recently been subject of much research (Zhou et al., 2012). In real everyday life, when we think of buying a particular and unfamiliar product, we most often tend to seek immediate advice from some of our friends who have come across this product or experienced it. We, similarly, tend to accept and use friends' recommendations because we trust them. Therefore, integrating social networks in recommender systems can result in more accurate recommendations (Rathod & Indiramma, 2015). In fact, interpersonal influence plays an important role in personalized recommender systems. It is found to be helpful in recommending items on social networks and it has as an objective relating recommended items to the person's historical behaviour and interpersonal relationship. The quality of the recommendation can be guaranteed based on the help of user interpersonal interests in a social network. Information obtained about users and their friends makes it unnecessary to look for similar users and to measure their rating similarity. Based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers and that trust among friends positively correlates with user preference, we decided to refer to research conducted on the emerging field of trust-based recommender system. Trust between two users, therefore, means that a user believes on the usefulness of the recommendation of a trusted user. This field of study focuses on providing users' personalized item recommendations with reference to the trust relationships among users, which is found to be useful in solving many of the issues associated with traditional systems, such as data sparsity (Papagelis et al., 2005) and cold start (Rathod & Indiramma, 2015).

Social trust-based recommendation systems are based on giving recommendations using only trust scores' calculation between users' interactions or on a combination of trust and similarity scores while giving suggestions. Golbeck (2009) argues that users prefer to receive recommendations from people they know and that trust-based recommendation approaches perform better than approaches based on only user similarity. If we like to search the most trusted friends in a social network for a userx, we need to compare all interactions between userx as well as each of his/her friends, taking into consideration the temporal factor of every interaction between them. That is because userx can trust a friend in the past, but the value of trust can degrade with time. In fact, interactions are not perceived the same way over time because some interactions are more important than others when computing an opinion (Haydar et al., 2015). That is way, time information can be useful in facilitating tracking the evolution of user interests and improving recommendation accuracy (Campos et al., 2014).

Ontologies are found to be useful in modelling different aspects of the world we live in. In the last decade, ontologies have been increasingly used in recommender systems (BlancoFernández et al., 2011). In the collaborative approach, domain ontologies are mainly used to analyse the user behaviour with reference to knowledge structure in order to build user profiles. In addition, ontologies are useful for modelling the trust between users extracted from a social network.

Our proposal relies on a combination of two approaches to improve the recommendation process. The first is the trusted friends' preference to reduce the sparsity problem when we do not have many information about the ego user. …

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