Title: Temporal dynamic recommendation based on data imputation through association analysis

Authors: Yuxiang Zhang; Xiayang Wang; Chunjing Xiao; Yu Sun; Yongwei Qiao

Addresses: School of Computer Science, Civil Aviation University of China, 2898 Jinbei Road, Dongli District, Tianjin 300300, China ' School of Computer Science, Civil Aviation University of China, 2898 Jinbei Road, Dongli District, Tianjin 300300, China ' School of Computer Science, Civil Aviation University of China, Tianjin China; School of Information Engineering, Hebei University of Technology, Tianjin China ' School of Computer Science, Civil Aviation University of China, Tianjin China ' Training Center of Engineering Technology, Civil Aviation University of China, Tianjin China

Abstract: As a novel method for modelling user interest drift over time, we explore the session-based temporal dynamic recommendation, in which we impute missing rating in terms of users' association. Firstly, we mine user association groups through association analysis according to users' common preferences. Secondly, the user's consumption history is divided into sessions, and we impute vacant values based on the correlation and occupation of user association groups in each session. Thirdly, we model the user interest drift over time by latent Dirichlet allocation (LDA) in each session and predict user's current interest by an exponential decay function. Finally, we predict ratings on items for active user using neighbour-based collaborative filtering (CF). Experiments on a real dataset show that the proposed framework is more effective than previous methods on several tasks.

Keywords: recommender system; interest drift; data sparsity; data imputation; association analysis.

DOI: 10.1504/IJCSE.2017.085966

International Journal of Computational Science and Engineering, 2017 Vol.15 No.1/2, pp.130 - 137

Received: 17 Jul 2015
Accepted: 29 Sep 2015

Published online: 21 Aug 2017 *

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