no code implementations • 26 Feb 2024 • Gaurav Verma, MinJe Choi, Kartik Sharma, Jamelle Watson-Daniels, Sejoon Oh, Srijan Kumar
It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models.
no code implementations • 5 Feb 2024 • Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data.
no code implementations • 12 Sep 2023 • Sejoon Oh, Walid Shalaby, Amir Afsharinejad, Xiquan Cui
However, the H-MTL framework has not been investigated in SBRSs yet.
no code implementations • 23 Sep 2022 • Walid Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui
Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items.
1 code implementation • 18 Aug 2022 • Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
Session-based recommender systems capture the short-term interest of a user within a session.
no code implementations • 29 Jan 2022 • Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar
We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data.
1 code implementation • 23 Aug 2021 • Sejoon Oh, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.