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NUS-WIDE: a real-world web image database from National University of Singapore
TLDR
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. Expand
Neural Collaborative Filtering
TLDR
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. Expand
Neural Factorization Machines for Sparse Predictive Analytics
TLDR
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. Expand
Neural Graph Collaborative Filtering
TLDR
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. Expand
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
TLDR
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. Expand
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
TLDR
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. Expand
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
TLDR
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. Expand
Visual Translation Embedding Network for Visual Relation Detection
TLDR
This work proposes a novel feature extraction layer that enables object-relation knowledge transfer in a fully-convolutional fashion that supports training and inference in a single forward/backward pass, and proposes the first end-toend relation detection network. Expand
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
TLDR
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. Expand
KGAT: Knowledge Graph Attention Network for Recommendation
TLDR
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. Expand
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