A fusion multi-criteria collaborative filtering algorithm for hotel recommendations.

Recommender systems employ information filtering techniques to mitigate the problem of generating personalised recommendations in a digital world that is heavily overloaded with information. Recently, the tourism industry becomes more and more popular and the number of online hotel booking sites with search engines has been growing. However, using such sites can be time consuming and burdensome for potential travellers. Accordingly, this paper proposes a fusion multi-criteria user-item collaborative filtering (MC-UICF) recommendation algorithm that exploits the multi-criteria ratings of users and integrates MC user-based CF and MC item-based CF techniques to produce personalised hotel recommendations. Experimental results on two real-world multi-criteria datasets show the effectiveness of the proposed algorithm by outperforming other baseline single-criteria and multi-criteria CF recommendation approaches in terms of recommendation accuracy and coverage, in particular, when dealing with sparse datasets.