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Recommendation system for sharing economy based on multidimensional trust modelopen access

Authors
Kim, SvetlanaYoon, YongIk
Issue Date
Dec-2016
Publisher
SPRINGER
Keywords
Multidimensional; Tensor factorization; Context-awareness; Rating; Recommendation; SFS
Citation
MULTIMEDIA TOOLS AND APPLICATIONS, v.75, no.23, pp 15297 - 15310
Pages
14
Journal Title
MULTIMEDIA TOOLS AND APPLICATIONS
Volume
75
Number
23
Start Page
15297
End Page
15310
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/9322
DOI
10.1007/s11042-014-2384-5
ISSN
1380-7501
1432-1882
Abstract
The recommendation system are widely adopted in today's mainstream online sharing services, providing useful prediction of user's rating or user's preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user's decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user's behavior based on rating analysis and identification of users' historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users' needs, are the focus of this paper.
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공과대학 (인공지능공학부)
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