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arXiv AI By Zidong Jia, Guofu Xie, Xiao Zhang

RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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