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Bo Wu
Researcher at Columbia University
Publications - 35
Citations - 695
Bo Wu is an academic researcher from Columbia University. The author has contributed to research in topics: Popularity & Artificial neural network. The author has an hindex of 9, co-authored 35 publications receiving 389 citations. Previous affiliations of Bo Wu include IBM & Chinese Academy of Sciences.
Papers
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Proceedings Article
Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition
TL;DR: To predict photo popularity, a novel framework named Multi-scale Temporal Decomposition (MTD) is proposed, which decomposes the popularity matrix in latent spaces based on contextual associations and models time-sensitive context on different time scales, which is beneficial to automatically learn temporal patterns.
Proceedings ArticleDOI
Sequential prediction of social media popularity with deep temporal context networks
TL;DR: A novel prediction framework called Deep Temporal Context Networks (DTCN) is proposed, which outperforms state-of-the-art deep prediction algorithms, and is designed to predict new popularity with temporal coherence across multiple time-scales.
Proceedings ArticleDOI
Time Matters: Multi-scale Temporalization of Social Media Popularity
TL;DR: Wang et al. as discussed by the authors proposed a multi-scale temporalization approach for predicting online popularity based on decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints.
Posted Content
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
TL;DR: Wang et al. as discussed by the authors proposed a deep temporal context network (DTCN) to predict the popularity of online content over time with sequential post streams of social media by incorporating both temporal context and temporal attention into account.
Proceedings ArticleDOI
GAIA: A Fine-grained Multimedia Knowledge Extraction System
Manling Li,Alireza Zareian,Ying Lin,Xiaoman Pan,Spencer Whitehead,Brian Chen,Bo Wu,Heng Ji,Shih-Fu Chang,Clare R. Voss,Daniel Napierski,Marjorie Freedman +11 more
TL;DR: The system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos, and achieves top performance at the recent NIST TAC SM-KBP2019 evaluation.