融合偏置深度学习的距离分解Top-N推荐算法  

Biased Deep Distance Factorization Algorithm for Top-N Recommendation

作　　者：钱梦薇 过弋[1,2,3] QIAN Meng-wei;GUO Yi(School of Information Science and Engineering,East University of Science and Technology,Shanghai 200237,China;Business Intelligence and Visualization Research Center,National Engineering Laboratory for Big Data Distribution and Exchange Technologies,Shanghai 200436,China;Shanghai Internet Big Data Engineering Technology Research Center,Shanghai 200072,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237 [2]大数据流通与交易技术国家工程实验室-商业智能与可视化技术研究中心,上海200436 [3]上海大数据与互联网受众工程技术研究中心,上海200072

出　　处：《计算机科学》2021年第9期103-109,共7页Computer Science

基　　金：国家重点研发计划(2018YFC0807105);国家自然科学基金项目(61462073);上海市科学技术委员会科研计划项目(17DZ1101003,18511106602,18DZ2252300)

摘　　要：针对传统矩阵分解算法大多是浅层的线性模型,难以学习到深层次的用户和物品的隐特征向量,且在数据稀疏的情况下容易产生过拟合的问题,文中提出一种融合偏置深度学习的矩阵分解算法,在解决数据稀疏问题的同时,还能学习到表征能力更强的距离特征向量。首先,通过用户与物品的显式和隐式数据构建用户与物品的交互矩阵,并将交互矩阵转化为相应的距离矩阵;其次,将距离矩阵分别按行和按列输入加入偏置层的深度神经网络,学习得到具有非线性特征的用户和物品的距离特征向量;最后,根据用户和物品的距离特征向量计算用户和物品之间的距离,用距离值对物品按升序排列,生成Top-N的推荐列表。在4个真实数据集上进行实验,采用Precision,Recall,MAP,MRR和NDCG指标进行评估,结果表明所提算法在上述指标方面相比其他主流推荐算法有明显提升。Since traditional matrix factorization algorithms are mostly based on shallow linear models,it is difficult to learn latent factors of users and items at a deep level.When the dataset is sparse,it is inclined to overfitting.To deal with the problem,this paper proposes a biased deep distance factorization algorithm,which can not only solve the data sparse problem,but also learn the distance feature vectors with stronger characterization capabilities.Firstly,the interaction matrix is constructed through the explicit and implicit data of the user and the item.Then the interaction matrix is converted into the corresponding distance matrix.Secondly,the distance matrix is input into the depth of the bias layer by row and column respectively.The neural network learns the distance feature vectors of users and items with non-linear features.Finally,the distance between the user and the item is calculated according to the distance feature vectors.Top-N item recommended list is generated according to the distance value.The experimental results show that Precision,Recall,MAP,MRR and NDCG of this algorithm are significantly improved compared to other mainstream recommendation algorithms on four different datasets.

关 键 词：偏置层 深度学习 距离分解 物品排序 

分 类 号：TP191[自动化与计算机技术—控制理论与控制工程]