RecSoup introduces a lightweight two‑stage method for Controllable Multi‑Objective Recommendation (CMOR) that merges multiple objective‑specific models without retraining. The approach first prunes parameters to reduce cross‑objective interference, then adaptively merges them using a learnable preference‑to‑coefficient mapping, enabling dynamic trade‑off control at inference time. Experiments show RecSoup achieves superior Pareto front quality and 165–228× faster inference than parameter‑generation baselines.
arXiv:2607. 19357v1 Announce Type: new Abstract: Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling.
DeGRe is a dense‑supervised generative reranking framework designed to improve multi‑stage recommender systems by addressing label bias and credit assignment issues. It uses an offline Lookahead Evaluator with beam search to generate dense supervision signals, which are distilled into a lightweight Online Generator that can perform efficient greedy decoding at inference time. Experiments show that DeGRe outperforms baselines on public benchmarks and industrial datasets, and it has been successfully deployed on Taobao Flash Shopping to enhance online recommendations.
arXiv:2607. 22665v1 Announce Type: new Abstract: Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interaction data.
arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.
Mixture-Trained Merging (MTM) is a method for creating unified language models that combine multiple objectives—such as mathematics, code, instruction following, and controllable thinking—into a single parameter set. Instead of sequentially post‑training on each objective, MTM trains each branch on a mixture of objectives, ensuring that the branches remain compatible in weight space and can be merged without degrading performance. The approach iteratively refines merge coefficients using low‑cost evaluations and multi‑objective Bayesian optimization, outperforming naive merging and preserving distinct behaviors across domains.
arXiv:2406. 09770v2 Announce Type: replace-cross Abstract: Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models.
arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.
arXiv:2509. 02555v2 Announce Type: replace-cross Abstract: Model merging techniques aim to integrate the abilities of multiple models into a single model.
arXiv:2609.37905v1 Announce Type: cross Abstract: Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical featur...
arXiv:2605. 21422v3 Announce Type: replace Abstract: As LLMs continue to scale up, improving training efficiency heavily relies on effective data utilization.
arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.
CoMerge is a conflict‑driven preference optimization framework for merging multiple expert language models into a single multi‑task model without full retraining. It treats model merging as a preference optimization problem, using self‑supervised, conflict‑driven hard negative samples derived from naive merging defects to refine lightweight, tensor‑wise merging coefficients. Experiments show CoMerge achieves near‑perfect performance on MergeBench and improves instruction‑following and safety on Llama‑3.1‑8B‑Instruct while optimizing only 1,445 scalar coefficients.