Search Results for author: Sejoon Oh

Found 6 papers, 2 papers with code

FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning

no code implementations5 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.

Recommendation Systems

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

no code implementations23 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.

Session-Based Recommendations

Rank List Sensitivity of Recommender Systems to Interaction Perturbations

no code implementations29 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.

Recommendation Systems

Influence-guided Data Augmentation for Neural Tensor Completion

1 code implementation23 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.

Data Augmentation Imputation +2

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