activity
20202026
most citedUser-oriented Fairness in Recommendation

214 citations · 1.2k across the 38 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.IR2020★ 89 cited

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

Yikun Xian, Zuohui Fu, Handong Zhao +8

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to gen…

cs.IR2020★ 19 cited

Learning Personalized Risk Preferences for Recommendation

Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…

cs.IR2020★ 122 cited

Understanding Echo Chambers in E-commerce Recommender Systems

Yingqiang Ge, Shuya Zhao, Honglu Zhou +4

Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper item…

cs.IR2020★ 17 cited

Fairness-Aware Explainable Recommendation over Knowledge Graphs

Zuohui Fu, Yikun Xian, Ruoyuan Gao +8

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in parti…

cs.CL2020

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

Zuohui Fu, Yikun Xian, Shijie Geng +5

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in…