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Deep Collaborative Filtering Combined with High-Level Feature Generation on Latent Factor Model

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

Abstract

Recommender System becomes indispensable in the era of information explosion nowadays. Former researchers have noticed the important role of high-level feature playing on semantic factor cases. However, in more common scenes where semantic features cannot be reached, research involving high-level feature on latent factor models is lacking. Analogizing to the idea of the convolutional neural network in image processing, we proposed a Weighted Feature Interaction Network to generate high-level features from the low-level latent factors. An intuitive interpretation is also given to help understand. Then it is integrated into a Deep Collaborative Filtering Model. The results on two real-world datasets show that weighted feature interaction network works and our Deep Collaborative Filtering Model outperforms some conventional and state-of-the-art models. Our work improves the feature representation and recommendation performance on Latent Factor Model.

Keywords

Recommender systems Latent factor model Collaborative filtering Deep neural network Implicit feedback 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of SoftwareTsinghua UniversityBeijingChina

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