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Neural Collaborative Filtering

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.
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SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

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.
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Attentive Moment Retrieval in Videos

An Attentive Cross-Modal Retrieval Network is developed that designs a memory attention mechanism to emphasize the visual features mentioned in the query and simultaneously incorporate their context, and obtains the augmented moment representation.
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Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

A novel attention mechanism in CF is introduced to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF), which significantly outperforms state-of-the-art CF methods.
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Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

A multimodal depressive dictionary learning model is proposed to detect the depressed users on Twitter and a series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines.
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MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

A Multi-modal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences is designed.
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Cross-modal Moment Localization in Videos

The proposed model, a language-temporal attention network is utilized to learn the word attention based on the temporal context information in the video and can automatically select "what words to listen to" for localizing the desired moment.
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Attentive Long Short-Term Preference Modeling for Personalized Product Search

This work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search, and significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.
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Denoising Implicit Feedback for Recommendation

This work proposes a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes noisy interactions during training and demonstrates that ADT significantly improves the quality of recommendation over normal training.
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Item Silk Road: Recommending Items from Information Domains to Social Users

This work presents a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs.
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