7 papers
Overview of the TREC 2025 Product Search and Recommendation Track
Dean E. Alvarez, Surya Kallumadi, Daniel Campos +4
In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimagina…
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4
On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates a…
Diversification as Risk Minimization
Rikiya Takehi, Fernando Diaz, Tetsuya Sakai
Users tend to remember failures of a search session more than its many successes. This observation has led to work on search robustness, where systems are penalized if they perform…
Fantastic (small) Retrievers and How to Train Them: mxbai-edge-colbert-v0 Tech Report
Rikiya Takehi, Benjamin Clavié, Sean Lee +1
In this work, we introduce mxbai-edge-colbert-v0 models, at two different parameter counts: 17M and 32M. As part of our research, we conduct numerous experiments to improve retriev…
Simple Projection Variants Improve ColBERT Performance
Benjamin Clavié, Sean Lee, Rikiya Takehi +2
Multi-vector dense retrieval methods like ColBERT systematically use a single-layer linear projection to reduce the dimensionality of individual vectors. In this study, we explore…
A General Framework for Off-Policy Learning with Partially-Observed Reward
Rikiya Takehi, Masahiro Asami, Kosuke Kawakami +1
Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under p…