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Markov逻辑网在基于信任的推荐系统中的应用

Markov logic networks with its application in trust based recommender systems
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摘要 基于信任的推荐系统是利用信任的实体进行项目推荐,然而信任是一个复杂的概念,对信任进行传播和预测是一项重要的任务。提出了用一种统计关系模型——Markov逻辑网来表示信任的传递性质,讨论了Markov逻辑网的理论模型,通过其推理算法预测信任关系,实验结果表明,在基于信任的推荐系统中Markov逻辑网方法比MoleTrust方法在推荐精度和解决冷用户问题上有更好的效果。 The trust based recommender system is to use the trusted entities to recommend items.As trust is a complex concept,to propagate and predict trust is an important task.A Statistical Relational Learning（SRL）model,Markov Logic Networks（MLNs）,is proposed to present the transfer properties of trust.The theory model of MLNs is discussed.With MLNs’s reasoning algorithm,the trust relationships are predicated.In the trust based recommender systems,the experimental results show that MLNs has a higher accuracy and better solution of cold-user problem than MoleTrust approach.
作者 熊忠阳 刘明 王勇 张玉芳 唐蓉君
机构地区 重庆大学计算机学院
出处 《计算机工程与应用》 CSCD 2012年第23期81-84,147,共5页 Computer Engineering and Applications
基金 中央高校研究生科技创新基金(No.CDJXS11180013) 重庆市自然科学基金(No.CSTC2008BB2191)
关键词 MARKOV逻辑网 信任 推荐系统 统计关系学习 Markov logic networks trust recommender systems statistical relational learning
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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计算机工程与应用
2012年 第23期

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