536 papers with code • 42 benchmarks • 31 datasets
The recommendation systems task is to produce a list of recommendations for a user.
( Image credit: CuMF_SGD )
State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example.
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
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
Ranked #1 on Click-Through Rate Prediction on Dianping
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.
Ranked #7 on Click-Through Rate Prediction on Bing News
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
These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.
Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction.
Learning effective feature crosses is the key behind building recommender systems.
Ranked #3 on Click-Through Rate Prediction on Criteo
By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications.
Ranked #5 on Click-Through Rate Prediction on Avazu