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Shangsong Liang
Researcher at Sun Yat-sen University
Publications - 75
Citations - 1784
Shangsong Liang is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Computer science & Topic model. The author has an hindex of 21, co-authored 63 publications receiving 1326 citations. Previous affiliations of Shangsong Liang include Northwest A&F University & University of Amsterdam.
Papers
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Proceedings ArticleDOI
Social Collaborative Viewpoint Regression with Explainable Recommendations
TL;DR: This paper proposes a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations, and uses so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations.
Proceedings ArticleDOI
Co-Embedding Attributed Networks
TL;DR: A Co-embedding model for Attributed Networks (CAN), which learns low-dimensional representations of both attributes and nodes in the same semantic space such that the affinities between them can be effectively captured and measured, and a variational auto-encoder that embeds each node and attribute with means and variances of Gaussian distributions.
Journal ArticleDOI
Multi-Order Attentive Ranking Model for Sequential Recommendation
TL;DR: A Multi-order Attentive Ranking Model (MARank) is proposed to unify both individual- and union-level item interaction into preference inference model from multiple views and significantly outperforms the state-of-the-art baselines on different evaluation metrics.
Proceedings ArticleDOI
Dynamic Clustering of Streaming Short Documents
TL;DR: A new dynamic clustering topic model - DCT - is proposed that enables tracking the time-varying distributions of topics over documents and words over topics, and overcomes the difficulty of handling short text by assigning a single topic to each short document.
Proceedings ArticleDOI
Personalized time-aware tweets summarization
TL;DR: A time-aware user behavior model is proposed, the Tweet Propagation Model (TPM), in which dynamic probabilistic distributions over interests and topics are inferred and an iterative optimization algorithm for selecting tweets is proposed.