ML Anthology

GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)

Michael Potter, Hamlin Liu, Yash Lala, Christian Loanzon, Yizhou Sun
AAAI 2022 pp. 13029-13030
doi:10.1609/AAAI.V36I11.21651 /aaai/2022/potter2022aaai-gru/

Abstract

We present a novel movie recommendation system, GRU4RecBE, which extends the GRU4Rec architecture with rich item features extracted by the pre-trained BERT model. GRU4RecBE outperforms state-of-the-art session-based models over the benchmark MovieLens 1m and MovieLens 20m datasets.

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Cite

Text

Potter et al. "GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21651

Markdown

[Potter et al. "GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/potter2022aaai-gru/) doi:10.1609/AAAI.V36I11.21651

BibTeX

@inproceedings{potter2022aaai-gru,
  title     = {{GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)}},
  author    = {Potter, Michael and Liu, Hamlin and Lala, Yash and Loanzon, Christian and Sun, Yizhou},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13029-13030},
  doi       = {10.1609/AAAI.V36I11.21651},
  url       = {https://mlanthology.org/aaai/2022/potter2022aaai-gru/}
}