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Online Shopping Recommendation with Bayesian Probabilistic Matrix Factorization

Abstract : Recommendation system plays a crucial role in demand prediction, arousing attention from industry, business, government and academia. Widely employed in recommendation system, matrix factorization can well capture the potential relationships between users, items and latent variables. In this paper, we focus on a specific recommendation task on the large scale opinion-sharing online dataset called Epinions. We carried out recommendation experiments with the Bayesian probabilistic matrix factorization algorithm and the final results showed the superior performance in comparison to six representative recommendation algorithms. Meanwhile, the Bayesian probabilistic matrix factorization was investigated in depth and the potential advantage was explained from the model flexibility in parameters’ adjustment. The findings would guide further research on applications of Bayesian probabilistic matrix factorization and inspire more researchers to contribute in this domain.
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https://hal.inria.fr/hal-01820909
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Submitted on : Friday, June 22, 2018 - 10:43:13 AM
Last modification on : Tuesday, September 3, 2019 - 3:04:02 PM
Long-term archiving on: : Tuesday, September 25, 2018 - 3:00:48 PM

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Jinming Wu, Zhong Liu, Guangquan Cheng, Qi Wang, Jincai Huang. Online Shopping Recommendation with Bayesian Probabilistic Matrix Factorization. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.445-451, ⟨10.1007/978-3-319-68121-4_48⟩. ⟨hal-01820909⟩

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