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

Michael Potter, Hamlin Liu, Yash Lala, Christian Loanzon, Yizhou Sun

[AAAI-22] Student Abstract and Poster Program

Keywords
Poster Session 1 @ Blue 5, Poster Session 11 @ Blue 5, Poster Session 1, Poster Session 11

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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.

Sessions where this paper appears

  • Poster Session 1

    Thu, February 24 4:45 PM - 6:30 PM (+00:00)
    Blue 5
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  • Poster Session 11

    Mon, February 28 12:45 AM - 2:30 AM (+00:00)
    Blue 5
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