Learning Distributed Representations for Recommender Systems with a Network Embedding Approach

  • Wayne Xin ZhaoEmail author
  • Jin Huang
  • Ji-Rong Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9994)


In this paper, we present a novel perspective to address recommendation tasks by utilizing the network representation learning techniques. Our idea is based on the observation that the input of typical recommendation tasks can be formulated as graphs. Thus, we propose to use the k-partite adoption graph to characterize various kinds of information in recommendation tasks. Once the historical adoption records have been transformed into a graph, we can apply the network embedding approach to learn vertex embeddings on the k-partite adoption network. Embeddings for different kinds of information are projected into the same latent space, where we can easily measure the relatedness between multiple vertices on the graph using some similarity measurements. In this way, the recommendation task has been casted into a similarity evaluation process using embedding vectors. The proposed approach is both general and scalable. To evaluate the effectiveness of the proposed approach, we construct extensive experiments on two different recommendation tasks using real-world datasets. The experimental results have shown the superiority of our approach. To the best of our knowledge, it is the first time that a network representation learning approach has been applied to recommendation tasks.


Recommender systems Network embedding Item recommendation Tag recommendation 



The authors thank the anonymous reviewers for their valuable and constructive comments. The work was partially supported by National Natural Science Foundation of China under the grant number 61502502 and Beijing Natural Science Foundation under the grant number 4162032.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsBeijingChina

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