Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation

Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simultaneously apply such information to recommendation services since the factors have interrelations to each other. In this paper, we propose a multi-criteria tensor model combining spatial and temporal information. The auxiliary information is categorized by several features and applied into the model. In particular, the spatial information of users’ countries is grouped into seven continents to reduce response times for learning the model. The single model enables to us keep the inherent structure of and the interrelations between multi-criteria and spatial/temporal information. To predict user preferences, tensor factorization based on Higher Order Singular Value Decomposition is exploited. Experimental results with a TripAdvisor dataset show that the proposed method outperforms other baseline methods based on a 2-dimensional rating matrix, tensor model, and other multi-criteria recommendation, in terms of RMSE and MAE. Furthermore, several experiments reveal the influences of the individual factors (i.e., multi-criteria, spatial and temporal information) and their consolidations, on restaurant recommendation. A comparative analysis of the multi-criteria elements shows that their influences relate to their correlations.