Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/119626
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Type: Journal article
Title: Item diversified recommendation based on influence diffusion
Author: Huang, H.
Shen, H.
Meng, Z.
Citation: Information Processing and Management, 2019; 56(3):939-954
Publisher: Elsevier
Issue Date: 2019
ISSN: 0306-4573
1873-5371
Statement of
Responsibility: 
Huimin Huang, Hong Shen, Zaiqiao Meng
Abstract: Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods.
Keywords: Item recommendation; influence diffusion; social networks
Rights: © 2019 Published by Elsevier Ltd.
RMID: 0030113554
DOI: 10.1016/j.ipm.2019.01.006
Grant ID: http://purl.org/au-research/grants/arc/DP150104871
Appears in Collections:Computer Science publications

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