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We propose a new model named LightGCN,including only the most essential component in GCN—neighborhood aggregation—for collaborative filtering. We provide three processed datasets: Gowalla, Yelp2018 and Amazon-book and one small dataset LastFM. see more in dataloader.py. run LightGCN on Gowalla dataset: Change ROOT_PATH in code/world.py.

2022年2月8日 · In this blog post, we will explore a state-of-the-art model fruited by the Graph Neural Network (GNN), LightGCN, and compare the performance of LightGCN and MF as movie recommender systems on the...

2025年2月20日 · The goal is to predict the next movie recommendation using a past movie watch and rating information. We will work with the Movielens dataset containing 100k ratings, as it has a manageable...

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Movie-Recommender-System-using-GNN • Performed necessary preprocessing on MovieLens dataset and implemented LightGCN with three hop network.• Trained with Self-Supervised and Supervised settings and then compared the results.

In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN,including only the most essential component in GCN—neighborhood aggregation—for collaborative filtering.

2020年2月6日 · In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time.

2023年9月22日 · 本文介绍了如何使用Python库PyTorch Geometric（PyG）将MovieLens数据集转换为图数据集，以利用图神经网络（GNN）进行推荐系统分析。 文章详细展示了如何构建节点和边，以及如何应用GCN模型进行特征提取和预测。 最近， 深度学习 在 推荐系统 领域取得了显著的进展。 其中， 图神经网络 （Graph Neural Networks，简称GNN）已经成为一种受欢迎的技术，用于处理包含丰富关系的数据，例如社交网络、知识图谱以及推荐系统。 在这篇文章中， …