Exploiting the roles of aspects in personalized POI recommender systems

• Autores: Ramesh Baral, Tao Li
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 32, Nº 2, 2018, págs. 320-343
• Idioma: inglés
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• Resumen

  • The evolution of World Wide Web (WWW) and the smart-phone technologies have revolutionized our daily life. This has facilitated the emergence of many useful systems, such as Location-based Social Networks (LBSN) which have provisioned many factors that are crucial for selection of Point-of-Interests (POI). Some of the major factors are: (i) the location attributes, such as geo-coordinates, category, and check-in time, (ii) the user attributes, such as, comments, tips, reviews, and ratings made to the locations, and (iii) other information, such as the distance of the POI from user’s house/office, social tie between users, and so forth. Careful selection of such factors can have significant impact on the efficiency of POI recommendation. In this paper, we define and analyze the fusion of different major aspects in POI recommendation. Such a fusion and analysis is barely explored by other researchers. The major contributions of this paper are: (i) it analyzes the role of different aspects (e.g., check-in frequency, social, temporal, spatial, and categorical) in the location recommendation, (ii) it proposes two fused models—a ranking-based, and a matrix factorization-based, that incorporate all the major aspects into a single recommendation model, and (iii) it evaluates the proposed models against two real-world datasets.