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Deep Tag Recommendation Based on Discrete Tensor Factorization

  • Wenwen YeEmail author
  • Zheng Qin
  • Xu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

In the recent years, tag recommendation is becoming more and more popular in both academic and industrial community. Although existing models have obtained great success in terms of enhancing the performance, an important problem has been ignored – the efficiency. To bridge this gap, in this paper, we design a novel discrete tensor factorization model (DTF) to encode user, item, tag into a unified hamming space for fast recommendations. More specifically, we first design a base model to translate the traditional pair-wise interaction tensor factorization (PITF) into its discrete version. Then, to provide our model with the ability to involve content information, we further extend the base model by introducing a deep content extractor for more comprehensive user/item profiling. Extensive experiments on two real-world data sets demonstrate that our model can greatly enhance the efficiency without sacrificing much effectiveness.

Keywords

Recommendation system Collaborative filtering Deep learning Tensor factorization Discrete 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of SoftwareTsinghua UniversityBeijingChina

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