Tag
Proposes a multi-objective reinforcement learning framework combining semantic embeddings with Pareto-DQN to balance engagement, diversity, and fairness in recommendations, mitigating filter bubbles.
OneRank proposes a Transformer-native multi-task ranking framework that integrates feature encoding and prediction to reduce inter-task interference and improve ranking performance in recommender systems.
This paper proposes a carbon-aware re-ranking strategy for e-commerce recommendations, using a retrieval-augmented pipeline to estimate product carbon footprints and trading off predicted engagement against sustainability. Evaluated on Amazon Reviews data, substantial carbon reductions are achievable with minimal engagement loss.
τ-Rec is a verifiable benchmark for agentic recommender systems that replaces subjective LLM-as-a-judge evaluations with verifiable rewards and controlled dialogue constraints, revealing steep reliability cliffs across leading models where even the best achieves only ~57% pass@1.
This paper proposes RankElastor, a novel architecture that mitigates embedding collapse in dense scaling of recommendation models by introducing parameterized full mixing and GLU-improved P-FFNs, achieving robust scaling and improved performance on large-scale datasets.
PEARL introduces a contrastive percentile approximation framework to mitigate behavioral intensity imbalance in recommender systems, achieving significant gains in engagement metrics in a production livestream platform serving billions of users.