43 citations · 119 across the 10 of their papers we have counts for
5 papers · 1 filter
Adversarial Text Rewriting for Text-aware Recommender Systems
Sejoon Oh, Gaurav Verma, Srijan Kumar
Text-aware recommender systems incorporate rich textual features, such as titles and descriptions, to generate item recommendations for users. The use of textual features helps mit…
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Sejoon Oh, Berk Ustun, Julian McAuley +1
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the…
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
Walid Shalaby, Sejoon Oh, Amir Afsharinejad +2
Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since…
Implicit Session Contexts for Next-Item Recommendations
Sejoon Oh, Ankur Bhardwaj, Jongseok Han +3
Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are…
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
Sejoon Oh, Berk Ustun, Julian McAuley +1
Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual dat…