no code implementations • 10 May 2023 • Shion Ishikawa, Yun Ching Liu, Young-joo Chung, Yu Hirate
Using a public dataset and internal carousel advertisement click dataset, we empirically show that item embedding with Latent Semantic Indexing (LSI) and Variational Auto-Encoder (VAE) improves the accuracy of position bias estimation and the estimated position bias enhances Learning to Rank performance.
no code implementations • 17 Apr 2023 • Md Mostafizur Rahman, Daisuke Kikuta, Satyen Abrol, Yu Hirate, Toyotaro Suzumura, Pablo Loyola, Takuma Ebisu, Manoj Kondapaka
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base.
no code implementations • 25 Aug 2022 • Shion Ishikawa, Young-joo Chung, Yu Hirate
We first show transferring knowledge and incorporating temporal dynamics improve the performance of the baseline models on a synthetic dataset.
no code implementations • 23 May 2022 • Daisuke Kikuta, Toyotaro Suzumura, Md Mostafizur Rahman, Yu Hirate, Satyen Abrol, Manoj Kondapaka, Takuma Ebisu, Pablo Loyola
The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems.
no code implementations • 11 Dec 2019 • Masaki Oguni, Yohei Seki, Yu Hirate
In user-generated recipe websites, users post their-original recipes.
1 code implementation • 15 Oct 2019 • Tianyu Li, Chien-Chih Wang, Yukun Ma, Patricia Ortal, Qifang Zhao, Bjorn Stenger, Yu Hirate
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model.
no code implementations • 17 Dec 2018 • Tianyu Li, Yukun Ma, Jiu Xu, Bjorn Stenger, Chen Liu, Yu Hirate
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems.