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.
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.
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.
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.
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.
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.
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 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.
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.
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.