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In particular, the spatial information of users\u2019 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.<\/jats:p>","DOI":"10.3233\/ais-200584","type":"journal-article","created":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T10:58:48Z","timestamp":1609239528000},"page":"5-19","source":"Crossref","is-referenced-by-count":11,"title":["Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation"],"prefix":"10.1177","volume":"13","author":[{"given":"Minsung","family":"Hong","sequence":"first","affiliation":[{"name":"Big Data Research Group, Western Norway Research Institute, Box 163, NO-6851 Sogndal, Norway. E-mail:\u00a0msh@vestforsk.no"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason J.","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Chung-Ang University, 84 Heukseok-ro, Heukseok-dong, Dongjak-gu, Seoul, South Korea. 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