摘要
利用隐式反馈信息实现个性化推荐是实用且具有挑战性的研究课题。对如何有效结合辅助信息来解决数据稀疏问题从而实现高效推荐的问题进行了研究,提出了一种融合node2vec和深度神经网络的隐式反馈推荐模型。该模型采用一种嵌入元数据的深度神经网络框架(Deep Neural Network Framework with Embedded Meta-data,Meta-DNN),首先将用户和项目的one-hot向量进行低维映射,再嵌入元数据信息,并结合node2vec的二阶随机游走方法学习网络中的邻居节点,使得相邻节点具有相似的节点表示,同时通过增强相邻用户和项目的平滑度来缓解数据稀疏性;最后使用深度神经网络进一步学习用户对项目的偏好,进而为用户产生推荐。其中,还引入了流行度参数对未知项目进行非平均抽样,优化隐式反馈负采样策略。在Gowalla和MovieLens-1M两个数据集上的实验表明,所提方法可以明显提高系统的预测性能和推荐质量。
It is extremely practical and challenging to implement personalized recommendation based on large-scale implicit feedback information.In order to solve the problem of data sparseness and then achieve effective recommendation combining various side information,this paper proposed an implicit feedback recommendation model combining node2vec and deep neural networks.This model utilizes a deep neural network framework with embedded meta-data(Meta-DNN),and maps the users and items vectors in a low-dimensional manner.Wherein,the second-order random walk of node2vec is used to learn neighbor nodes in the network with embedded meta-data so that adjacent nodes have similar node representations.Besides,data sparsity is alleviated via improving the smoothness among neighboring users and items.Finally,deep neural network is used for further learning user preferences for items,thus providing recommendation for users.In addition,the popularity parameter is introduced to perform non-average sampling of unknown items and an implicit feedback negative sampling strategy is optimized.Experimental results on the typical Gowalla and Mo-vieLens-1M data sets demonstrate the prediction performance and recommendation quality of the proposed model compared with state-of-the-art recommendation algorithms.
作者
何瑾琳
刘学军
徐新艳
毛宇佳
HE Jin-lin;LIU Xue-jun;XU Xin-yan;MAO Yu-jia(School of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)
出处
《计算机科学》
CSCD
北大核心
2019年第6期41-48,共8页
Computer Science
基金
江苏省重点研发计划项目(BE2017617,BE2015697)
国家重点研发计划子课题(2017YFC0805605)资助
