most citedAdap-: Adaptively Modulating Embedding Magnitude for Recommendation

29 citations · 68 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023

Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis

Dan Ma, Jun Xu, Zongyu Wang +2

Product reviews often contain a large number of implicit aspects and object-attribute co-existence cases. Unfortunately, many existing studies in Aspect-Based Sentiment Analysis (A…

cs.CV20231 cited

Exchanging-based Multimodal Fusion with Transformer

Renyu Zhu, Chengcheng Han, Yong Qian +5

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from…

cs.CL202310 cited

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Chengcheng Han, Renyu Zhu, Jun Kuang +5

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represent…

cs.LG20231 cited

FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion

Wentao Shi, Junkang Wu, Xuezhi Cao +4

Learning hyperbolic embeddings for knowledge graph (KG) has gained increasing attention due to its superiority in capturing hierarchies. However, some important operations in hyper…

cs.IR202329 cited

Adap-: Adaptively Modulating Embedding Magnitude for Recommendation

Jiawei Chen, Junkang Wu, Jiancan Wu +3

Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these meth…

cs.SI201527 cited

Revealing Multiple Layers of Hidden Community Structure in Networks

Kun He, Sucheta Soundarajan, Xuezhi Cao +2

We introduce a new conception of community structure, which we refer to as hidden community structure. Hidden community structure refers to a specific type of overlapping community…