Item diversified recommendation based on influence diffusion |
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| Affiliation: | 1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;2. School of Computer Science, University of Adelaide, Adelaide, Australia;1. Department of Computer Science, University of Vigo, ESEI, Campus As Lagoas, 32004 Ourense, Spain;2. The Biomedical Research Centre (CINBIO), Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain;3. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain;4. Centre of Biological Engineering (CEB), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;1. RMIT University, Melbourne, Australia;2. University of Warwick, Coventry, UK;3. Kno.e.sis Center, Wright State University, Dayton, Ohio, USA;4. RWTH Aachen University, Aachen, Germany;5. GESIS–Leibniz Institute for the Social Sciences, Cologne, Germany;1. School of Computer Science, South China Normal University, Guangzhou, China;2. Department of SEEM, The Chinese University of Hong Kong, Hong Kong;3. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;5. School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, China;1. University of Bergen, Norway;2. Norwegian University of Science and Technology, Norway;3. University of Helsinki |
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| 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. |
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