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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 668–681 | Cite as

Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

  • Lin Yue
  • Xiao-Xin Sun
  • Wen-Zhu Gao
  • Guo-Zhong Feng
  • Bang-Zuo Zhang
Regular Paper
  • 42 Downloads

Abstract

With the ever-growing dynamicity, complexity, and volume of information resources, the recommendation technique is proposed and becomes one of the most effective techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

Keywords

semantic representation plot information denoising autoencoder collaborative filtering auxiliary information 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and TechnologyNortheast Normal UniversityChangchunChina
  2. 2.Key Laboratory of Applied Statistics of Ministry of EducationNortheast Normal UniversityChangchunChina
  3. 3.School of EnvironmentNortheast Normal UniversityChangchunChina
  4. 4.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina

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