Title | Structure | Dataset | Baseline | Metric |
---|---|---|---|---|
Atentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (ACF) [2017 SIGIR(ACM)**] [paper] |
Pinterest Vine |
[CF-based] UCF itemKNN BPR SVD++ [Content-based] CBF [Hybrid] SVDFeature Deep Hybrid |
HR NDCG (L-O-O*) (K=10~100) (dim=32,64,128) |
|
Deep Matrix Factorization Models for Recommender Systems (DMF) [2017 IJCAI**] [paper] |
ML 100K ML 1m Amusic Amovie |
itemPOP itemKNN eALS NeuMF-p |
HR NDCG (L-O-O*) (K=10) |
|
Neural Collaborative Filtering (NeuMF) [2017 WWW**] [paper] |
ML 1m |
itemPOP itemKNN BPR eALS |
HR NDCG (L-O-O*) (K=1~10) (dim=8,16,32,64) |
|
A Neural Collaborative Filtering Model with Interaction-based Neighborhood (NNCF) [2017 CIKM(ACM)**] [paper] |
Delicious ML 1m Rossmann |
itemPOP itemKNN BPR NeuMF |
HR NDCG (L-O-O*) (K=5,10) (dim=32) |
|
NAIS: Neural Attentive Item Similarity Model for Recommendation (NAIS) [2018 IEEE] [paper] |
ML 1m (same set as NCF) |
itemPOP itemKNN FISM MF-BPR MF-eALS MLP |
HR NDCG (L-O-O*) (K=10) (dim=8,16,32,64) |
|
Outer Product-based Neural Collaborative Filtering (ConvNCF) [2018 arXiv] [paper] |
Yelp Gowalla |
itemPOP MF-BPR MLP JRL NeuMF |
HR NDCG (L-O-O-1000) (K=10) (dim=64) |
|
Deep Item-based Collaborative Filtering for Top-N Recommendation (DeepICF) [2019 ACM] [paper] |
ML 1m |
itemPOP itemKNN HOSLIM BPR eALS MLP FISM |
HR NDCG (L-O-O*) (K=10) (dim=8,16,32,64) |
|
Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network (DAM) [2019 IJCAI**] [paper] |
Netease Youshu |
BPR NCF BR EFM |
Recall MAP (L-O-O*) (K=5) (dim=5,10) |
|
Neural Graph Collaborative Filtering (NGCF) [2019 SIGIR(ACM)**] [paper] |
Gowalla Yelp2018 Amazon-Book |
MF NeuMF CMN HOP-Rec PinSage GC-MC |
Recall NDCG (tt-split, 8:2) (K=20) |
|
Reinforced Negative Sampling for Recommendation with Exposure Data (RNS) [2019 IJCAI**] [paper] |
Beibei Zhihu |
[common] itemPOP BPR-GMF [adversarial sampler] BPR-DNS KBGAN IRGAN [exposure-enhanced samplers] BPE-EN EBPR |
AUC NDCG (L-O-O*) (K=10) (dim=32) |
|
Efficient Neural Matrix Factorization without Sampling for Recommendation (ENMF) [2020 ACM] [paper] |
Ciao Epinion ML 1m |
itemPOP itemKNN BPR WMF ExpoMF GMF NCF ConvNCF |
HR NDCG (L-O-O-all) (K=50,100,200) (dim=8,16,32,64) |
* : L-O-O : Leave-one-Out
** : Conference Proceedings Paper
Title | Structure | Dataset | Baseline | Metric |
---|---|---|---|---|
Deep Learning over Multi-field Categorical Data (FNN,SNN) [2016 ECIR**] [paper] |
iPinYou | LR FM FNN(ours) SNN(ours) |
AUC-ROC | |
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features (DC) [2016 KDD**] [paper] |
text_cp1_tn_s text_cp1_tn_b |
DSSM | Logloss | |
Wide & Deep Learning for Recommender Systems (W&D) [2016 DLRS**] [paper] |
- | wide deep wide&deep |
offline AUC | |
Deep & Cross Network for Ad Click Predictions (DCN) [2017 ADKDD**] [paper] |
Criteo Display Ads2 | DNN LR FM W&D DC |
Logloss | |
Neural Factorization Machines for Sparse Predictive Analytics (NFM) [2017 SIGIR**] [paper] |
Frappe ML-(2017) |
LibFM HOFM Wide&Deep DeepCross |
RMSE (tt-split, 7:2:1) |
|
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (DeepFM) [2017 arXiv] [paper] |
Criteo Company* |
LR,FM FNN IPNN,OPNN,PNN* LR&DNN FM&DNN |
AUC-ROC Logloss |
|
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (xDeepFM) [2018 SIGKDD**] [paper] |
Criteo Dianping Bing News |
LR,FM DNN,DCN W&D PNN DeepFM |
AUC-ROC Logloss |
|
CFM: Convolutional Factorization Machines for Context-Aware Recommendation (CFM) [2019 IJCAI] [paper] |
Frappe Last FM ML 1m |
itemPOP FM NFM DeepFM ONCF |
HR NDCG L-O-O-all (K=5,10,20) |
|
HoAFM: A High-order Attentive Factorization Machine for CTR Prediction (HoAFM) [2020 IP&M] [paper] |
Criteo Avazu |
GBDT+LR FM W&D NFM CIN xDeepFM |
AUC-ROC Logloss (tt-split,8:1:1) |
* : L-O-O : Leave-one-Out (100)
** : Conference Proceedings Paper
Title | Structure | Dataset | Baseline | Metric |
---|
* : L-O-O : Leave-one-Out (100)
** : Conference Proceedings Paper
Title | Structure | Dataset | Baseline | Metric |
---|---|---|---|---|
KGAT: Knowledge Graph Attention Network for Recommendation (KGAT) [2019 SIGKDD**] [paper] |
Amazon-book Last-FM Yelp2018 |
FM NFM CKE CFKG MCRec RippleNet GC-MC |
recall NDCG (L-O-O-all) (K=20) (dim=64) |
|
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences (KTUP) [2019 WWW**] [paper] |
ML-1m DBbook2014 |
CFKG CKE CoFM |
Precision Recall F1 Hit NDCG (tt-split,7:1:2) (K=10) (dim=100) |
|
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (KGNN) [2019 SIGKDD**] [paper] |
||||
Knowledge Graph Convolutional Networks for Recommender Systems (KGCN) [2019 WWW] [paper] |
* : L-O-O : Leave-one-Out (100)
** : Conference Proceedings Paper