A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization - Nilashi, Mehrbakhsh (Elsevier, 2015)
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.