activity
20172022
most citedContrastive Learning for Recommender System

45 citations · 95 across the 10 of their papers we have counts for

collaborators

15 papers

cs.IR202145 cited

Contrastive Learning for Recommender System

Zhuang Liu, Yunpu Ma, Yuanxin Ouyang +1

Recommender systems, which analyze users' preference patterns to suggest potential targets, are indispensable in today's society. Collaborative Filtering (CF) is the most popular r…

cs.CL2021

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

Yiran Xing, Zai Shi, Zhao Meng +3

We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal in…

cs.LG20204 cited

DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Zhen Han, Yunpu Ma, Peng Chen +1

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Tempo…

cs.LG202019 cited

xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

Zhen Han, Peng Chen, Yunpu Ma +1

Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on t…

cs.LG2020

Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score

Zhiliang Wu, Yinchong Yang, Yunpu Ma +4

Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…

cs.LG2020

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

Zhen Han, Yunpu Ma, Yuyi Wang +2

The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work has generalized the Hawkes process to a neurall…