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Neural Collaborative Filtering
TLDR
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. Expand
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Exploring temporal effects for location recommendation on location-based social networks
TLDR
We introduce a novel location recommendation framework, based on the temporal properties of user movement observed from a real-world LBSN dataset. Expand
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Label Informed Attributed Network Embedding
TLDR
We propose a novel label informed Attributed Network Embedding (LANE) framework. Expand
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Accelerated Attributed Network Embedding
TLDR
In this paper, we propose an accelerated attributed network embedding algorithm AANE, which enables the joint learning process to be done in a distributed manner by decomposing the complex modeling and optimization into many sub-problems. Expand
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Exploiting homophily effect for trust prediction
TLDR
We investigate the trust prediction problem with the homophily effect in an unsupervised setting and propose an optimization problem integrated with it to solve the problem. Expand
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Auto-Keras: An Efficient Neural Architecture Search System
TLDR
Neural architecture search with network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. Expand
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Social recommendation: a review
TLDR
We present a review of existing recommender systems and discuss some research directions to improve social recommendation capabilities. Expand
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Unsupervised sentiment analysis with emotional signals
TLDR
We propose to study the problem of unsupervised sentiment analysis with emotional signals in social media. Expand
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Content-Aware Point of Interest Recommendation on Location-Based Social Networks
TLDR
The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). Expand
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Exploiting Local and Global Social Context for Recommendation
TLDR
We develop approaches to capture local and global social relations, and propose a novel framework LOCABAL taking advantage of both local andglobal social context for recommendation. Expand
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