A novel hotel recommendation method based on personalized preferences and implicit relationships |
| |
| Affiliation: | 1. Faculty of Economics and Business Administration, Ruppin Academic Center, Israel;2. Paris School of Business, France;1. École des Sciences de Gestion de l’Université du Québec à Montréal (ESG UQAM), 320, rue Sainte-Catherine Est, Montréal, QC, H2X 3X2, Canada;2. University of Perpignan, CRESEM (EA 7397), 52 avenue Paul Alduy, 66860, Perpignan, France;3. École de Gestion de l’Université du Québec à Trois-Rivières (EGUQTR), 3351, boulevard des Forges, Trois-Rivières, QC, G9A 5H7, Canada;1. Department of Marketing, Auckland University of Technology, 120 Mayoral Drive, Auckland 1010, New Zealand;2. Department of Marketing, Spears School of Business, Oklahoma State University, 477 Business Building, Stillwater, OK, 74078, USA;3. School of Hotel & Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Kowloon, Hong Kong;4. College of International Management, Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu, Oita, 874-8577, Japan;1. Gabelli School of Business, Fordham University, 140 W. 62nd Street, New York, NY, 10023, United States;2. Koppelman School of Business, Brooklyn College of the City University of New York, 2900 Bedford Ave, Brooklyn, NY, 11210, United States;1. Economics and Management School, Shanghai Maritime University No.1550, Haigang Avenue, Pudong District, Shanghai, 201902 China;2. Cardiff Business School, Cardiff University Cardiff, UK;3. International College of Intellectual Property, Tongji University, Shanghai, China;1. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR, China;2. Kemmons Wilson School of Hospitality & Resort Management, The University of Memphis, Memphis, TN 38152, United States;3. School of Hospitality & Tourism Management, University of Surrey, Guildford, Surrey, GU2 7XH, UK;4. School of Hotel, Restaurant and Tourism Management, University of South Carolina, Columbia, SC 29208, United States;5. School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China |
| |
| Abstract: | On tourism websites, hotel recommendations have drawn growing attention from researchers, as they can help customers select a satisfactory hotel from many options with massive information. However, some inherent challenges exist in conventional hotel recommendations, specifically the extent to which there is considerable room for improvement in user preference models and neighbour recognition. Therefore, we propose a two-stage hotel recommendation approach that employs hotel feature information to support preference analysis. First, in the filling stage, association rules between features are considered to accurately capture users’ personalized preferences, which can be incorporated with public preferences to estimate potential ratings of users for unvisited hotels. Then, in the recommendation stage, we combine rating similarities between users with their closeness relationships to identify more reliable neighbours. Finally, a hotel recommendation case on Ctrip.com is performed to evaluate the model. Experimental results confirm that our method outperforms the other five benchmark methods. |
| |
| Keywords: | Hotel recommendations Hotel feature information Association rules Personalized preferences Closeness relationships |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
