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

Authors

  • Michael Potter University of California Los Angeles
  • Hamlin Liu University of California Los Angeles
  • Yash Lala University of California Los Angeles
  • Christian Loanzon University of California Los Angeles
  • Yizhou Sun University of California Los Angeles

DOI:

https://doi.org/10.1609/aaai.v36i11.21651

Keywords:

Session-Based Recommendation, Bidirectional Encoder Representations From Transformers, Gated Recurrent Unit, Movie Recommendations, Ranking

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.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Potter, M., Liu, H., Lala, Y., Loanzon, C., & Sun, Y. (2022). GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13029-13030. https://doi.org/10.1609/aaai.v36i11.21651

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program