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NEAR: Normalized Network Embedding with Autoencoder for Top-K Item Recommendation

  • Dedong Li
  • Aimin ZhouEmail author
  • Chuan Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.

Keywords

Network embedding Recommendation system Autoencoder Heterogeneous network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  2. 2.School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina

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