Abstract: 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.

Key words: Deep learning, Implicit feedback, Meta-data, Neural networks, Node2vec, Recommender system