7 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.IR2019★ 6 cited
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…
cs.IR2019★ 7 cited
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…
cs.IR2017★ 7 cited
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…