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cs.IR2025
Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems
Timo Wilm, Philipp Normann
A critical challenge in recommender systems is to establish reliable relationships between offline and online metrics that predict real-world performance. Motivated by recent advan…
cs.IR2025
Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
Timo Wilm, Philipp Normann, Felix Stepprath
This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural networ…
cs.IR2025
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
Timo Wilm, Philipp Normann, Sophie Baumeister +1
This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailin…