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            <title xml:lang="en">Online Shopping Recommendation with Bayesian Probabilistic Matrix Factorization</title>
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                <forename type="first">Jinming</forename>
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                <title xml:lang="en">Online Shopping Recommendation with Bayesian Probabilistic Matrix Factorization</title>
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                  <title>2nd International Conference on Intelligence Science (ICIS)</title>
                  <date type="start">2017-10-25</date>
                  <date type="end">2017-10-28</date>
                  <settlement>Shanghai</settlement>
                  <country key="CN">China</country>
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                <editor>Zhongzhi Shi</editor>
                <editor>Ben Goertzel</editor>
                <editor>Jiali Feng</editor>
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                  <publisher>Springer International Publishing</publisher>
                  <biblScope unit="serie">Intelligence Science I</biblScope>
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                <term xml:lang="en">Monte Carlo Markov chain</term>
                <term xml:lang="en">Bayesian probabilistic matrix factorization</term>
                <term xml:lang="en">Recommendation system</term>
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              <p>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.</p>
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          <orgName>National University of Defense Technology [China]</orgName>
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              <addrLine>Hunan, Changsha, Kaifu</addrLine>
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