conftrace_

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 - Artificial Intelligence and Data Science & Analytics

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 pre-trained language model recommender system session-based recommendation movie recommendation hybrid recommendation system pre-trained bert

More from AAAI 2022

All AAAI 2022 papers →

Explainable Shapley-Based Allocation (Student Abstract)

Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract)

Grad-Align: Gradual Network Alignment via Graph Neural Networks (Student Abstract)

CL-NERIL: A Cross-Lingual Model for NER in Indian Languages (Student Abstract)

Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)

XDC: Adversarial Adaptive Cross Domain Face Clustering (Student Abstract)