Qi Liu

TL;DR: A novel deep learning framework for multivariate time series classification is proposed that is not only more efficient than the state of the art but also competitive in accuracy and demonstrates that feature learning is worth to investigate for time series Classification.

TL;DR: This paper establishes a labeled heterogeneous sentiment dataset which consists of users» sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method, and proposes a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users» latent representations from heterogeneous networks and predict the sign of unobserved sentiment links.

TL;DR: This paper proposes a general Exercise-Enhanced Recurrent Neural Network framework and extends EERNN to an explainable Exercise-aware Knowledge Tracing framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix.

TL;DR: The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is thus much more effective than traditional recommendation methods for travel package recommendation.

TL;DR: The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests.