A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS

•A multi-criteria CF recommender system in tourism domain is proposed.•Predictive accuracy of multi-criteria CF recommender systems is improved.•EM algorithm, ANFIS and PCA are applied in the proposed method.•PCA is applied for solving multi-collinearity problem.•ANFIS is applied for developing the prediction models.

In order to improve the tourist experience, recommender systems are used to offer personalized information for online users. The hotel industry is a leading stakeholder in the tourism sector, which needs to provide online facilities to their customers. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and they can be an appropriate choice for the tourist. In this paper, we propose a new hybrid method for hotel recommendation using dimensionality reduction and prediction techniques. Accordingly, we have developed the multi-criteria CF recommender systems for hotel recommendation to enhance the predictive accuracy by using Gaussian mixture model with Expectation Maximization (EM) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS). We have also used the Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF dataset. Our experiments confirmed that the proposed hybrid method achieved high accuracy for hotel recommendation for the tourism sector.

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