440 citations · 1.3k across the 33 of their papers we have counts for
36 papers
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…
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
Yikun Xian, Zuohui Fu, Qiaoying Huang +2
Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide expla…
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, S. Muthukrishnan +2
Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing app…
Waterfall Bandits: Learning to Sell Ads Online
Branislav Kveton, Saied Mahdian, S. Muthukrishnan +2
A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that…
Offline Evaluation of Ranking Policies with Click Models
Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton +3
Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies…
Stochastic Low-Rank Bandits
Branislav Kveton, Csaba Szepesvari, Anup Rao +3
Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix…