1 citations · 1 across the 3 of their papers we have counts for
3 papers
Progressive Content Refinement with Decaying Reward Joint LinUCB
Shion Ishikawa, Pablo Loyola, Young-joo Chung +1
Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Refine to traditional bandit appr…
Position Bias Estimation with Item Embedding for Sparse Dataset
Shion Ishikawa, Yun Ching Liu, Young-Joo Chung +1
Estimating position bias is a well-known challenge in Learning to Rank (L2R). Click data in e-commerce applications, such as targeted advertisements and search engines, provides im…
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
Shion Ishikawa, Young-joo Chung, Yu Hirate
Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and e…