activity
20162019
most citedDeep Session Interest Network for Click-Through Rate Prediction

39 citations · 105 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CL201917 cited

Improving Multi-turn Dialogue Modelling with Utterance ReWriter

Hui Su, Xiaoyu Shen, Rongzhi Zhang +4

Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challe…

cs.IR20191 cited

POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Wen Chen, Pipei Huang, Jiaming Xu +7

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashi…

cs.IR20192 cited

Exact-K Recommendation via Maximal Clique Optimization

Yu Gong, Yu Zhu, Lu Duan +5

This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (const…

cs.IR201939 cited

Deep Session Interest Network for Click-Through Rate Prediction

Yufei Feng, Fuyu Lv, Weichen Shen +4

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and e…

cs.CV20195 cited

Low-Power Computer Vision: Status, Challenges, Opportunities

Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…

cs.IR201911 cited

Personalized Re-ranking for Recommendation

Changhua Pei, Yi Zhang, Yongfeng Zhang +6

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optim…