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2020 – today
- 2024
[c30]Guangyuan Piao, Michalis Mountantonakis, Panagiotis Papadakos, Pournima Sonawane, Aidan O'Mahony:
Toward Exploring Knowledge Graphs with LLMs. SEMANTiCS (Posters, Demos, Workshops & Tutorials) 2024- 2023
- [c29]Weipeng Fuzzy Huang
, Junjie Tao, Changbo Deng, Ming Fan, Wenqiang Wan, Qi Xiong, Guangyuan Piao:
Rényi Divergence Deep Mutual Learning. ECML/PKDD (2) 2023: 156-172 - 2022
- [i5]Weipeng Fuzzy Huang, Junjie Tao, Changbo Deng, Ming Fan, Wenqiang Wan, Qi Xiong, Guangyuan Piao:
Rényi Divergence Deep Mutual Learning. CoRR abs/2209.05732 (2022) - 2021
- [j3]Gopika Premsankar, Guangyuan Piao, Patrick K. Nicholson, Mario Di Francesco, Diego Lugones:
Data-Driven Energy Conservation in Cellular Networks: A Systems Approach. IEEE Trans. Netw. Serv. Manag. 18(3): 3567-3582 (2021) - [c28]Siddhesh Chaubal, Mateusz Rzepecki, Patrick K. Nicholson, Guangyuan Piao, Alessandra Sala:
Geometric Heuristics for Transfer Learning in Decision Trees. CIKM 2021: 151-160 - [c27]Guangyuan Piao:
Recommending Knowledge Concepts on MOOC Platforms with Meta-path-based Representation Learning. EDM 2021 - [c26]Guangyuan Piao:
A Simple Language Independent Approach for Distinguishing Individuals on Social Media. HT 2021: 251-256 - [c25]Shanti Chilukuri, Guangyuan Piao, Diego Lugones, Dirk Pesch:
Deadline-Aware TDMA Scheduling for Multihop Networks Using Reinforcement Learning. Networking 2021: 1-9 - [c24]Guangyuan Piao:
Scholarly Text Classification with Sentence BERT and Entity Embeddings. PAKDD (Workshops) 2021: 79-87 - [c23]Weipéng Huáng, Nishma Laitonjam, Guangyuan Piao, Neil J. Hurley:
Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach. PAKDD (3) 2021: 206-218 - [c22]Guangyuan Piao, Weipéng Huáng:
Learning to Predict the Departure Dynamics of Wikidata Editors. ISWC 2021: 39-55 - 2020
- [j2]Fattane Zarrinkalam, Stefano Faralli, Guangyuan Piao, Ebrahim Bagheri:
Extracting, Mining and Predicting Users' Interests from Social Media. Found. Trends Inf. Retr. 14(5): 445-617 (2020) - [c21]Weipéng Huáng, Guangyuan Piao, Raúl Moreno, Neil Hurley:
Partially Observable Markov Decision Process Modelling for Assessing Hierarchies. ACML 2020: 641-656 - [c20]Fattane Zarrinkalam, Guangyuan Piao, Stefano Faralli, Ebrahim Bagheri:
Mining User Interests from Social Media. CIKM 2020: 3519-3520 - [c19]Guangyuan Piao, Patrick K. Nicholson, Diego Lugones:
Env2Vec: accelerating VNF testing with deep learning. EuroSys 2020: 41:1-41:16
2010 – 2019
- 2019
- [i4]Weipéng Huáng, Nishma Laitonjam, Guangyuan Piao, Neil Hurley:
Bayesian Hierarchical Mixture Clustering using Multilevel Hierarchical Dirichlet Processes. CoRR abs/1905.05022 (2019) - [i3]Weipéng Huáng, Guangyuan Piao, Raúl Moreno, Neil J. Hurley:
Evaluating Hierarchies through A Partially Observable Markov Decision Processes Methodology. CoRR abs/1908.07031 (2019) - 2018
- [j1]Guangyuan Piao, John G. Breslin:
Inferring user interests in microblogging social networks: a survey. User Model. User Adapt. Interact. 28(3): 277-329 (2018) - [c18]Guangyuan Piao, John G. Breslin:
A Study of the Similarities of Entity Embeddings Learned from Different Aspects of a Knowledge Base for Item Recommendations. DL4KGS@ESWC 2018: 2-13 - [c17]Guangyuan Piao, John G. Breslin:
Domain-Aware Sentiment Classification with GRUs and CNNs. SemWebEval@ESWC 2018: 129-139 - [c16]Guangyuan Piao, John G. Breslin:
A Study of the Similarities of Entity Embeddings Learned from Different Aspects of a Knowledge Base for Item Recommendations. ESWC (Satellite Events) 2018: 345-359 - [c15]Guangyuan Piao, John G. Breslin:
Transfer Learning for Item Recommendations and Knowledge Graph Completion in Item Related Domains via a Co-Factorization Model. ESWC 2018: 496-511 - [c14]Guangyuan Piao, John G. Breslin:
Learning to Rank Tweets with Author-Based Long Short-Term Memory Networks. ICWE 2018: 288-295 - [c13]Guangyuan Piao, John G. Breslin:
Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach. WWW (Companion Volume) 2018: 1973-1977 - 2017
- [c12]Guangyuan Piao, John G. Breslin:
Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies. ECIR 2017: 122-133 - [c11]Guangyuan Piao, John G. Breslin:
Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter. HT 2017: 155-164 - [c10]Guangyuan Piao, John G. Breslin:
Factorization Machines Leveraging Lightweight Linked Open Data-Enabled Features for Top-N Recommendations. WISE (2) 2017: 420-434 - [i2]Guangyuan Piao, John G. Breslin:
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations. CoRR abs/1707.05651 (2017) - [i1]Guangyuan Piao, John G. Breslin:
Inferring User Interests in Microblogging Social Networks: A Survey. CoRR abs/1712.07691 (2017) - 2016
- [c9]Guangyuan Piao, John G. Breslin:
User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations. CIKM 2016: 2057-2060 - [c8]Guangyuan Piao, John G. Breslin:
Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter. EKAW 2016: 496-510 - [c7]Guangyuan Piao, John G. Breslin:
Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations. SEMANTiCS 2016: 81-88 - [c6]Guangyuan Piao, John G. Breslin:
Measuring semantic distance for linked open data-enabled recommender systems. SAC 2016: 315-320 - [c5]Guangyuan Piao:
Exploiting the semantic similarity of interests in a semantic interest graph for social recommendations: student research abstract. SAC 2016: 375-376 - [c4]Guangyuan Piao, John G. Breslin:
Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations. UMAP 2016: 105-109 - [c3]Guangyuan Piao, John G. Breslin:
Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations. UMAP 2016: 291-292 - [c2]Guangyuan Piao:
Towards Comprehensive User Modeling on the Social Web for Personalized Link Recommendations. UMAP 2016: 333-336 - 2015
- [c1]Guangyuan Piao, Safina Showkat Ara, John G. Breslin:
Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes. JIST 2015: 185-200
Coauthor Index
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