5 citations · 5 across the 5 of their papers we have counts for
5 papers
Decomposition and Interleaving for Variance Reduction of Post-click Metrics
Kojiro Iizuka, Yoshifumi Seki, Makoto P. Kato
In this study, we propose an efficient method for comparing the post-click metric (e.g., dwell time and conversion rate) of multiple rankings in online experiments. The proposed me…
Theoretical Analysis on the Efficiency of Interleaved Comparisons
Kojiro Iizuka, Hajime Morita, Makoto P. Kato
This study presents a theoretical analysis on the efficiency of interleaving, an efficient online evaluation method for rankings. Although interleaving has already been applied to…
The Effect of News Article Quality on Ad Consumption
Kojiro Iizuka, Yoshifumi Seki, Makoto P. Kato
Practical news feed platforms generate a hybrid list of news articles and advertising items (e.g., products, services, or information) and many platforms optimize the position of n…
CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming
Junjie H. Xu, Yu Nakano, Lingrong Kong +1
Billions of live-streaming viewers share their opinions on scenes they are watching in real-time and interact with the event, commentators as well as other viewers via text comment…
Greedy Optimized Multileaving for Personalization
Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki
Personalization plays an important role in many services. To evaluate personalized rankings, online evaluation, such as A/B testing, is widely used today. Recently, multileaving ha…