🎁 Recommender Systems

🧠 NeurIPS2026 · 2 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (24) · 💬 ACL2026 (22) · 🧪 ICML2026 (11) · 🤖 AAAI2026 (27) · 🧠 NeurIPS2025 (24) · 🧪 ICML2025 (17)

Goal-Conditioned Supervised Learning for Multi-Objective Recommendation

MOGCSL preserves multiple future session rewards as a goal vector, trains a goal-conditioned next-item predictor with ordinary cross-entropy, and selects inference goals using statistics or two CVAEs, improving purchase prediction and training cost without dominating every click metric or guaranteeing that specified goals are attainable.

Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models

The paper reframes personalized generation as selecting suitable candidates already produced by a generator, using an MLP with 2.8M parameters by default to rank frozen generator representations; it beats four generalist reward models across nine datasets but remains behind a dataset-finetuned 8B reward model, while single-trajectory decoding guidance recovers only part of the selection gain.