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Ruiming Tang
Researcher at Huawei
Publications - 178
Citations - 4429
Ruiming Tang is an academic researcher from Huawei. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 19, co-authored 116 publications receiving 2294 citations. Previous affiliations of Ruiming Tang include The Chinese University of Hong Kong & National University of Singapore.
Papers
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Proceedings ArticleDOI
DeepFM: a factorization-machine based neural network for CTR prediction
TL;DR: 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.
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
TL;DR: DeepFM as mentioned in this paper combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture, which has a shared input to its "wide" and "deep" parts.
Journal ArticleDOI
Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
TL;DR: Zhang et al. as discussed by the authors proposed Product-based Neural Network (PIN), which adopts a feature extractor to explore feature interactions and generalizes the kernel product to a net-in-net architecture.
Proceedings ArticleDOI
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
TL;DR: A novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier, which significantly outperforms nine state-of-the-art models on three large-scale datasets.
Journal ArticleDOI
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
TL;DR: Wang et al. as discussed by the authors propose a tree-structured policy gradient recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree.