Yu Nakano / 中野優

生成検索エンジン最適化に関する研究の紹介

論文紹介: COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at Amazon (SIGMOD 2024)

論文紹介: PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models (WSDM 2024)

論文紹介: Accelerating Learned Sparse Indexes Via Term Impact Decomposition (Findings of EMNLP 2022)

論文紹介: Transformer Memory as a Differentiable Search Index (NeurIPS 2022)

The ACM Web Conference 2022 国際会議報告

論文紹介: 1. Societal Biases in Retrieved Contents: Measurement Frameworkand Adversarial Mitigation of BERT Rankers (SIGIR 2021)2. Leveraging Lead Bias for Zero-shot Abstractive News Summarization (SIGIR 2021) / IR-Reading-2021-fall

論文紹介: An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes (RecSys 2021)

論文紹介: Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study (WSDM 2021)

論文紹介: Web Table Retrieval using Multimodal Deep Learning (SIGIR 2020) Table Search Using a Deep Contextualized Language Model (SIGIR 2020) / ir-reading-2020-fall

論文紹介: Ad Hoc Table Retrieval using Intrinsic and Extrinsic Similarities (TheWebConf 2020) / ir-reading-2020-spring