14 citations · 27 across the 6 of their papers we have counts for
6 papers
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
Ziwei Zhang, Chenhao Niu, Peng Cui +3
Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs…
Learning Semantic Vector Representations of Source Code via a Siamese Neural Network
David Wehr, Halley Fede, Eleanor Pence +4
The abundance of open-source code, coupled with the success of recent advances in deep learning for natural language processing, has given rise to a promising new application of ma…
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
Dongyang Zhao, Liang Zhang, Bo Zhang +3
The recommender system is an important form of intelligent application, which assists users to alleviate from information redundancy. Among the metrics used to evaluate a recommend…
COSINE: Compressive Network Embedding on Large-scale Information Networks
Zhengyan Zhang, Cheng Yang, Zhiyuan Liu +4
There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. As there are many large-scale real-world networks, it's inefficient for existing…
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Xun Liu, Wei Xue, Lei Xiao +1
We describe a parallel bayesian online deep learning framework (PBODL) for click-through rate (CTR) prediction within today's Tencent advertising system, which provides quick and a…