NeuRec: On Nonlinear Transformation for Personalized Ranking

Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong

IJCAI 2018 pp. 3669-3675

doi:10.24963/IJCAI.2018/510 /ijcai/2018/zhang2018ijcai-neurec/

Abstract

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

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Text

Zhang et al. "NeuRec: On Nonlinear Transformation for Personalized Ranking." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/510

Markdown

[Zhang et al. "NeuRec: On Nonlinear Transformation for Personalized Ranking." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/zhang2018ijcai-neurec/) doi:10.24963/IJCAI.2018/510

BibTeX

@inproceedings{zhang2018ijcai-neurec,
  title     = {{NeuRec: On Nonlinear Transformation for Personalized Ranking}},
  author    = {Zhang, Shuai and Yao, Lina and Sun, Aixin and Wang, Sen and Long, Guodong and Dong, Manqing},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {3669-3675},
  doi       = {10.24963/IJCAI.2018/510},
  url       = {https://mlanthology.org/ijcai/2018/zhang2018ijcai-neurec/}
}