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 • 2 Dec 2022 • Yang Shi, Guannan Liang, Young-joo Chung
Training samples in RS can be highly biased toward popular businesses with sufficient sales and can decrease advertising performance for small businesses.
1 code implementation • 31 Aug 2022 • Ramin Raziperchikolaei, Young-joo Chung
In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a large number of pairs with unknown interactions.
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 • 18 Apr 2022 • Yang Shi, Young-joo Chung
We conducted experiments on MovieLens1M, Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed the existing matrix factorization-based methods.
no code implementations • 14 Mar 2022 • Ramin Raziperchikolaei, Young-joo Chung
Then, we propose to learn the value of the non-zero elements of the inputs jointly with the neural network parameters.
1 code implementation • 15 Feb 2022 • Yang Shi, Young-joo Chung
We simultaneously learn binary hash codes and quantization codes to preserve semantic information in multiple modalities by an end-to-end deep learning architecture.
no code implementations • 13 Mar 2021 • Robin Swezey, Young-joo Chung
We introduce an approach to recommending short-lived dynamic packages for golf booking services.
1 code implementation • 12 Oct 2020 • Ramin Raziperchikolaei, Tianyu Li, Young-joo Chung
We also apply the NRP framework to a direct neural network structure which predicts the ratings without reconstructing the user and item information.