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Neural Collaborative Filtering
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
- Computer ScienceWWW
- 3 April 2017
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Neural Graph Collaborative Filtering
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang
- Computer ScienceSIGIR
- 6 February 2020
This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.
Neural Factorization Machines for Sparse Predictive Analytics
NFM seamlessly combines the linearity of FM in modelling second- order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions, and is more expressive than FM since FM can be seen as a special case of NFM without hidden layers.
KGAT: Knowledge Graph Attention Network for Recommendation
This work proposes a new method named Knowledge Graph Attention Network (KGAT), which explicitly models the high-order connectivities in KG in an end-to-end fashion and significantly outperforms state-of-the-art methods like Neural FM and RippleNet.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua
- Computer ScienceIJCAI
- 1 August 2017
A novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network, which consistently outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross with a much simpler structure and fewer model parameters.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
A new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique is designed, for efficiently optimizing a Matrix Factorization (MF) model with variably-weighted missing data and exploiting this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback.
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
TriRank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews, and allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.
Video Question Answering via Gradually Refined Attention over Appearance and Motion
This paper proposes an end-to-end model which gradually refines its attention over the appearance and motion features of the video using the question as guidance and demonstrates the effectiveness of the model by analyzing the refined attention weights during the question answering procedure.
Adversarial Personalized Ranking for Recommendation
This work proposes a new optimization framework, namely Adversarial Personalized Ranking (APR), which enhances the pairwise ranking method BPR by performing adversarial training and achieves state-of-the-art performance for item recommendation.