T
Tat-Seng Chua
Researcher at National University of Singapore
Publications - 807
Citations - 52797
Tat-Seng Chua is an academic researcher from National University of Singapore. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 90, co-authored 706 publications receiving 36628 citations. Previous affiliations of Tat-Seng Chua include Xi'an Jiaotong University & Singapore General Hospital.
Papers
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Proceedings ArticleDOI
Neural Collaborative Filtering
TL;DR: 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.
Proceedings ArticleDOI
NUS-WIDE: a real-world web image database from National University of Singapore
TL;DR: The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval and four research issues on web image annotation and retrieval are identified.
Proceedings ArticleDOI
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
TL;DR: This paper introduces a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN that significantly outperforms state-of-the-art visual attention-based image captioning methods.
Proceedings ArticleDOI
Neural Graph Collaborative Filtering
TL;DR: Wang et al. as discussed by the authors proposed 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.
Journal ArticleDOI
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
TL;DR: This paper presents a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics, and presents the prevalent Hadoop framework for addressing big data challenges.