引用本文:王建芳,苗艳玲,韩鹏飞,等.一种融合信任和项目卷积描述信息的PMF算法[J].控制与决策,2020,35(8):1803-1812
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一种融合信任和项目卷积描述信息的PMF算法
王建芳,苗艳玲,韩鹏飞,司马海峰
(河南理工大学计算机科学与技术学院,河南焦作454000)
摘要:
针对现有概率矩阵分解(PMF)技术的个性化推荐系统在采用社交网络中信任信息时常常忽视项目相关描述文档信息的问题,提出一种融合用户信任和通过卷积网络以获取项目描述等信息的PMF模型.首先,利用用户偏好信息和行为轨迹信息构建一种新的信任网络;然后,通过卷积神经网络从项目描述文档中提取项目潜在的特征向量;最后,在概率矩阵分解过程中同时利用评分数据、信任网络中用户的信任信息和项目的描述信息,计算用户和项目的潜在特征向量以预测评分并进行个性化推荐.为验证算法的有效性,选择3种算法在4个数据集上进行对比,实验结果表明所提出的算法在推荐精确度和鲁棒性方面优于其他3种算法.
关键词:  推荐算法  卷积神经网络  社交网络  信任  概率矩阵分解
DOI:10.13195/j.kzyjc.2018.1678
分类号:TP391
基金项目:国家自然科学基金项目(61602157);河南省高等学校重点科研项目(15A520074).
A PMF algorithm based on trust and item convolutional description information
WANG Jian-fang,MIAO Yan-ling,HAN Peng-fei,SIMA Hai-feng
(School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo454000,China)
Abstract:
The existing personalized recommendation algorithm for the probabilistic matrix factorization(PMF) usually ignores the description document information of the hidden items when using trust information in social networks. A PMF algorithm is proposed based on improved trust and item convolutional description information(ITC-PMF). Firstly, a new trust network is constructed by using user preferences and behavior trajectory information. Then, the item latent features are extracted from contextual documents by convolutional neural network(CNN). Furthermore, based on the PMF, the rating records, the trust information and the item description information are simultaneously used to calculate the latent feature vectors of the users and the items, so as to predict and make personalized recommendations. Finally, to verify effectiveness of the ITC-PMF algorithm, three other state-of-the-art algorithms are compared on four real-world datasets. Experimental results show that the proposed algorithm outperforms other three algorithms in terms of recommendation accuracy and robustness.
Key words:  recommender  convolutional neural network  social network  trust  probabilistic matrix factorization

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