Proposed PinSage, combining random walks and graph convolutions, scales to 3 billion nodes and 18 billion edges for web-scale recommendation.

Rex Ying, Ruining He, Kaifeng Chen et al.

Proposes Dual Learning Algorithm (DLA) for joint unbiased estimation of propensity and ranking models, outperforming randomized unbiased methods.

Qingyao Ai, Keping Bi, Cheng Luo et al.

This paper analyzes the impact of hyperparameters in Word2Vec's Skip-gram negative sampling for recommendation, showing significant performance gains through optimization.

Hugo Caselles-Dupré, Florian Lesaint, Jimena Royo-Letelier

EGES integrates side information and reaches 0.9746 Taobao AUC, a 10.8% gain over BGE.

Jizhe Wang, Pipei Huang, Huan Zhao et al.

Proposes NARM, combining attention and sequence encoding, achieving significant improvements in long session recommendations.

Jing Li, Pengjie Ren, Zhumin Chen et al.

Introduces CausE algorithm using causal embeddings to optimize recommendation systems, achieving significant performance improvements.

Stephen Bonner, Flavian Vasile

DeepFM combines FM and DNN for end-to-end CTR prediction, capturing both low- and high-order feature interactions.

Huifeng Guo, Ruiming Tang, Yunming Ye et al.

Proposes Propensity SVM-Rank, using inverse propensity scoring for unbiased learning from biased implicit feedback.

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel