Authors: Xiaona Xia; Zheng Qin; Jiguo Yu; Lianyong Qi
Addresses: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China ' School of Software, Tsinghua University, Beijing, 100084, China ' School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China ' School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
Abstract: Personalised service recommendation is the key technology for service platforms; the demand preferences of users are the important factors for personalised recommendation. First, in order to improve accuracy and adaptability of service recommendation, services are needed to be initialised before being recommended and selected, then they are classified and clustered according to demand preferences, and service clusters are defined and demonstrated. In the sparse problem of service function matrix, historical and potential preferences are expressed as double matrices. Second, service cluster is viewed as the basic business unit, we optimise graph summarisation algorithm and construct service recommendation algorithm SCRP, helped by the experiments about variety parameters, which has more advantages than other algorithms. Third, we select fuzzy degree and difference to be the two key indicators, and use some service clusters to complete simulating and analyse algorithm performances. The results show that our service selection and recommendation method is better than others, which might effectively improve the quality of service recommendation.
Keywords: service clustering; service recommendation; graph summarisation algorithm; personalisation; preference matrix.
International Journal of Computational Science and Engineering, 2019 Vol.18 No.2, pp.176 - 185
Received: 18 Jul 2016
Accepted: 07 Aug 2016
Published online: 14 Feb 2019 *