No abstract

No abstract

No abstract

Abstract. Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k -Nearest Neighbours (k -NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv ) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.