129 citations · 136 across the 4 of their papers we have counts for
4 papers
How Powerful is Graph Filtering for Recommendation
Shaowen Peng, Xin Liu, Kazunari Sugiyama +1
It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a…
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-bas…
Abstractive Meeting Summarization UsingDependency Graph Fusion
Siddhartha Banerjee, Prasenjit Mitra, Kazunari Sugiyama
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries. To improve this, we propose an approach to…
Multi-document abstractive summarization using ILP based multi-sentence compression
Siddhartha Banerjee, Prasenjit Mitra, Kazunari Sugiyama
Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we ai…