%0 Conference Proceedings
%T Online Shopping Recommendation with Bayesian Probabilistic Matrix Factorization
%+ National University of Defense Technology [China]
%A Wu, Jinming
%A Liu, Zhong
%A Cheng, Guangquan
%A Wang, Qi
%A Huang, Jincai
%Z Part 6: Intelligent Applications
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 2nd International Conference on Intelligence Science (ICIS)
%C Shanghai, China
%Y Zhongzhi Shi
%Y Ben Goertzel
%Y Jiali Feng
%I Springer International Publishing
%3 Intelligence Science I
%V AICT-510
%P 445-451
%8 2017-10-25
%D 2017
%R 10.1007/978-3-319-68121-4_48
%K Recommendation system
%K Bayesian probabilistic matrix factorization
%K Monte Carlo Markov chain
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-01820909/document
%2 https://inria.hal.science/hal-01820909/file/978-3-319-68121-4_48_Chapter.pdf
%L hal-01820909
%U https://inria.hal.science/hal-01820909
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-AICT-510
%~ IFIP-ICIS