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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He
- Computer ScienceInternational Joint Conference on Artificial…
- 13 March 2017
This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
Federated Meta-Learning with Fast Convergence and Efficient Communication
This work proposes a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches, and achieves a reduction in required communication cost and increase in accuracy as compared to Federated Averaging.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
This work proposes a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS), which can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
- Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He
- Computer ScienceInternational Conference on Information and…
- 26 October 2021
This paper proposes an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation and resorts to directly approximate the limit of infinite-layer graph convolutions via a constraint loss.
Federated Meta-Learning for Recommendation
Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
This work investigates the potential of leveraging knowledge graph (KG) in dealing with issues of RL methods for IRS, which provides rich side information for recommendation decision making and makes use of the prior knowledge of the item correlation learned from KG to guide the candidate selection for better candidate item retrieval.
Neighbor Interaction Aware Graph Convolution Networks for Recommendation
- Jianing Sun, Yingxue Zhang, M. Coates
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 25 July 2020
A novel framework NIA-GCN is proposed, which can explicitly model the relational information between neighbor nodes and exploit the heterogeneous nature of the user-item bipartite graph, and generalize to a commercial App store recommendation scenario.
Multi-graph Convolution Collaborative Filtering
- Jianing Sun, Yingxue Zhang, Xiuqiang He
- Computer ScienceIndustrial Conference on Data Mining
- 1 November 2019
This work develops a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process and quantitatively verifies the effectiveness of each component of the proposed model.
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong
- Computer ScienceArXiv
- 12 April 2018
It is shown that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and the proposed framework, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
- Zhu Zhang, Zhijie Lin, Zhou Zhao, Jieming Zhu, Xiuqiang He
- Computer ScienceACM Multimedia
- 19 August 2020
This paper proposes a novel Regularized Two-Branch Proposal Network to simultaneously consider the inter-sample and intra-sample confrontments and applies the proposal regularization to stabilize the training process and improve model performance.