Mendeley TY _ JOUR ID - 13971108167191 TI - DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback JO - Journal of Information Systems and Telecommunication (JIST) JA - ES LA - en SN - 2322-1437 AU - Kolahkaj Maral AU - Harounabadi Ali AU - Nikravan shalmani Alireza AU - Chinipardaz Rahim AD - Islamic Azad University, Soosangerd AD - Islamic Azad university, Central Tehran Branch AD - Karaj Branch, Islamic Azad University, Karaj, Iran AD - University of Shahid Chamran, Ahwaz, Iran Y1 - 2019 PY - 2019 VL - 24 IS - 6 SP - 209 EP - 219 KW - Decision Support Systems KW - KW - Data Mining KW - KW - Context-aware Recommendation KW - KW - Geo-tagged Photo KW - KW - Asymmetric Similarity KW - DO - N2 - By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures. UR - rimag.ir/en/Article/15215 L1 - rimag.ir/en/Article/Download/15215 ER -