A Novel Top-N Recommendation Approach Based on Conditional Variational Auto-Encoder

  • Bo Pang
  • Min Yang
  • Chongjun WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


Personalized recommendation has continuously received attention due to its great commercial value in business. Recently variational auto-encoder is employed in top-N recommendation for its effectiveness in deep collaborative filtering. The key challenge of model-based collaborative filtering is to develop effective latent factors representations with user-item interaction records. In this paper, we present a new class of conditional variational auto-encoders (CVAEs) that utilizes the fact of similar users tending to associate with each other on purchasing preference. This type of conditional variational auto-encoder concentrates on learning with label verification signals to ensure an exclusive latent mean factor for users with the same labels. Moreover, to handle complex multi-label combinations, we extend the model with a split-merge framework by learning labels of different conditional attributes separately and then merge the results from multiple prediction pools. Extensive experiments are conducted on two real-life datasets to simulate both user-based and item-based recommendation scenarios. Experimental results are favorable when comparing with the state-of-art methods.


Recommender systems Collaborative filtering Variational auto-encoder 



This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YF- B1001102), the National Natural Science Foundation of China (Grant Nos. 61502227, 61876080), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Software InstituteJilin UniversityJilinChina

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