7 citations · 31 across the 7 of their papers we have counts for
7 papers
Obj-GloVe: Scene-Based Contextual Object Embedding
Canwen Xu, Zhenzhong Chen, Chenliang Li
Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference. I…
A Capsule Network for Recommendation and Explaining What You Like and Dislike
Chenliang Li, Cong Quan, Li Peng +3
User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for…
A Review-Driven Neural Model for Sequential Recommendation
Chenliang Li, Xichuan Niu, Xiangyang Luo +2
Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting use…
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
Shiqian Chen, Chenliang Li, Feng Ji +2
The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify…
A Context-Aware User-Item Representation Learning for Item Recommendation
Libing Wu, Cong Quan, Chenliang Li +2
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent repr…
Multi-label Dataless Text Classification with Topic Modeling
Daochen Zha, Chenliang Li
Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques…