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Aixin Sun
Researcher at Nanyang Technological University
Publications - 291
Citations - 13080
Aixin Sun is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Web query classification. The author has an hindex of 49, co-authored 255 publications receiving 10251 citations. Previous affiliations of Aixin Sun include NICTA & Zhengzhou University.
Papers
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Proceedings ArticleDOI
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction
Longke Hu,Aixin Sun,Yong Liu +2 more
TL;DR: Using the business review data from Yelp, this paper studies business rating prediction and shows that by incorporating geographical neighborhood influences, much lower prediction error is achieved than the state-of-the-art models including Biased MF, SVD++, and Social MF.
Journal ArticleDOI
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings
TL;DR: This work proposes two more effective topic models for short texts, named GPU-DMM and GPU-PDMM, and demonstrates that PDMM achieves better topic representations than state-of-the-art models, measured by topic coherence.
Proceedings ArticleDOI
Comments-oriented blog summarization by sentence extraction
Meishan Hu,Aixin Sun,Ee-Peng Lim +2 more
TL;DR: This research aims to extract representative sentences from a blog post that best represent the topics discussed among its comments, using ReQuT to derive representative words from comments and then selects sentences containing representative words.
Proceedings ArticleDOI
Comments-oriented document summarization: understanding documents with readers' feedback
Meishan Hu,Aixin Sun,Ee-Peng Lim +2 more
TL;DR: The proposed summarization methods utilizing comments showed significant improvement over those not using comments, and the methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.
Proceedings ArticleDOI
Quality-aware collaborative question answering: methods and evaluation
TL;DR: This research addresses this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers by proposing a quality-aware framework to design methods that select answers from acommunity QA portal considering answer quality in addition to answer relevance.