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2022 AAAI AAAI 2022

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

Abstract

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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Deep Learning and Machine Learning
🧭 Keyword Pioneer — hybrid recommendation system
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Data Science & Analytics > Applications > Recommender Systems Deep Learning > Learning Types > Deep Learning Machine Learning > Application Areas > Recommender Systems Deep Learning > Learning Types > Sequence Modeling

Keywords

sequence modeling feature extraction recommender system pretrained language model session-based recommendation movie recommendation hybrid recommendation system pre-trained bert
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