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贝叶斯网络在用户兴趣模型构建中的研究 被引量:22

Research on User Interest Model Construction Based on Bayes Network
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摘要 用户兴趣模型对于用户画像的刻画至关重要。用户画像是用户在互联网中的身份证,完整地构建用户画像能够相对明确地知晓用户需求,这对于互联网时代提升用户体验非常重要。众所周知,电商购物、新闻视频推荐等众多领域都需要清晰地刻画用户画像,根据用户的兴趣定向推荐相关内容。 User interest model is essential for the user portrait depicts.User portrait is the Internet ID card of user, which completely builds user portrait to relatively clear awareness of the needs of users, and it is very important to improve the user experience in Internet age. As we all know, shopping, news video recommendation and other areas are required to clearly portray the user portrait, according to the user's interest oriented recommendation related content.
作者 王庆福
机构地区 辽宁行政学院
出处 《无线互联科技》 2016年第12期101-102,共2页 Wireless Internet Technology
关键词 用户画像 兴趣模型 内容推荐 user profile interest model content recommendation
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  • 1李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20. 被引量:1277
  • 2史建国,高晓光.离散动态贝叶斯网络的直接计算推理算法[J].系统工程与电子技术,2005,27(9):1626-1630. 被引量:36
  • 3高晓光,史建国.变结构离散动态贝叶斯网络及其推理算法[J].系统工程学报,2007,22(1):9-14. 被引量:22
  • 41.Chickering D. Learning equivalence classes of Bayesian networks structures. In: Horvitz E, Jensen F ed. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1996. 54~61
  • 52.Geriger D, Hekerman D. A charactererization of the Dirichlet distribution with application to learning Bayesian networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 1995. 196~207
  • 63.Heckman D. A Bayesian approach for learning causal networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1995. 285~295
  • 74.Heckman D, Geiger D, Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 1995,20(3):197~243
  • 85.Heckman D, Shachter R. Decision-Theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 1995,3:405~430
  • 96.Heckman D, Mandani A, Wellman M. Real-World applications of Bayesian networks. Communications of the ACM, 1995,38(3):38~45
  • 107.Buntine W. Theory refinement on Bayesian networks. In: Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann Publishers, Inc., 1991. 52~61

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