202 papers with code • 0 benchmarks • 0 datasets
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #2 on Recommendation Systems on Gowalla
When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.
Ranked #1 on Recommendation Systems on Pinterest
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings.
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems.
Ranked #1 on Recommendation Systems on MovieLens 10M
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.