Title: Service recommendation using conditional restricted Boltzmann machines

Authors: Tianyang Li; Ting He; Zhongjie Wang

Addresses: School of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nangang District, Harbin, China ' College of Computer Science and Technology, Huaqiao University, No. 668 Jimei Avenue, Jimei District, Xiamen, China ' School of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nangang District, Harbin, China

Abstract: We propose methods based on the conditional restricted Boltzmann machine (CRBM) for the service recommendation. First, we construct a CRBM model, the individualised characteristics of customers and indexes of satisfaction have been encoded into its conditional units, and the using status of services has been encoded into its visible units. Next, a method for dynamically adjusting learning rates is proposed to improve the training process of the CRBM. Finally, we develop a neighbourhood-based approach to further boost recommendation results. The evaluation on a dataset extracted from a manufacturing company, validates that the above-proposed methods have highly practical relevance to the service recommendation problem in real world business.

Keywords: service recommendation; the conditional restricted Boltzmann machine; CRBM; service; restricted Boltzmann machine; RBM; learning rate.

DOI: 10.1504/IJSTM.2019.101896

International Journal of Services Technology and Management, 2019 Vol.25 No.5/6, pp.423 - 442

Received: 15 Jul 2017
Accepted: 29 Sep 2017
Published online: 30 Aug 2019 *

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