GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items

Chenglei Shen; Teng Shi; Weijie Yu; Xiao Zhang; Jun Xu

cs.IRMar 15, 2026

GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items

Authors: Chenglei Shen, Teng Shi, Weijie Yu, Xiao Zhang, Jun Xu

Organizations: Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · School of Information Technology and Management, University of International Business and Economics, Beijing, China

Abstract

Generative recommendation (GR) has shown strong potential for sequential recommendation in an end-to-end generation paradigm. However, existing GR models suffer from severe cold-start collapse: their recommendation accuracy on cold-start items can drop to near zero. Current solutions typically rely on retraining with cold-start interactions, which is hindered by sparse feedback, high computational cost, and delayed updates, limiting practical utility in rapidly evolving recommendation catalogs. Inspired by model editing in NLP, which enables training-free knowledge injection into large language models, we explore how to bring this paradigm to generative recommendation. This, however, faces two key challenges: GR lacks the explicit subject-object binding common in natural language, making targeted edits difficult; and GR does not exhibit stable token co-occurrence patterns, making the injection of multi-token item representations unreliable. To address these challenges, we propose GenRecEdit, a model editing framework tailored for generative recommendation. GenRecEdit explicitly models the relationship between the full sequence context and next-token generation, adopts iterative token-level editing to inject multi-token item representations, and introduces a one-to-one trigger mechanism to reduce interference among multiple edits during inference. Extensive experiments on multiple datasets show that GenRecEdit substantially improves recommendation performance on cold-start items while preserving the model's original recommendation quality. Moreover, it achieves these gains using only about 9.5% of the training time required for retraining, enabling more efficient and frequent model updates.

Figures & tables

Figure 1. An illustration of cold-start collapse in GR. The left panel presents dataset statistics that characterize cold-start collapse, while the right panel summarizes the adverse effects on cold-start items. The bottom panel shows the time-efficiency of GenRecEdit in terms of model update cost.

Figure 2. An illustration of the challenges adapting model editing from NLP to generative recommendation (GR).

Figure 3. The analysis of the cold-start collapse. Left: NDCG at the first $n$ positions when a GR model generates a four-position semantic ID on the Cell Phones and Accessories dataset. Right: Distribution of generated items regardless of recommendation correctness; a higher IID Ratio indicates a larger fraction of generated items that belong to the current test subset (i.e., cold subset or warm subset).

Figure 4. Overall framework of GenRecEdit, which consists of three main modules: (1) Position-Wise Knowledge Preparation. We construct pseudo interaction data for cold-start items and use it to form edit requests. (2) Locate-Then-Edit Framework. For each position, we first use a classifier to localize the key layer that is most strongly associated with that position, and then perform targeted edits on the identified layer. (3) One-One Triggering Policy. To prevent interference among edits for different positions, we adopt an adaptive triggering strategy at inference time, where the corresponding edit layer is triggered according to the current decoding position.

Metric	Item ID-based				Semantic ID-based						Cold-start-based
	SASRec		BERT4Rec		VQ-Rec		TIGER		LC-Rec		Retrain		Finetune		SpecGR		Ours
	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold	Overall	Cold
Video
NDCG@10	0.0014	–	0.0019	–	0.0027	0.0000	0.0070	0.0020	0.0055	0.0012	0.0083	0.0044	0.0076	0.0103	0.0114	0.0051	0.0118	0.0123
NDCG@20	0.0019	–	0.0028	–	0.0035	0.0000	0.0096	0.0030	0.0072	0.0017	0.0109	0.0058	0.0093	0.0125	0.0136	0.0062	0.0140	0.0141
NDCG@50	0.0027	–	0.0044	–	0.0046	0.0001	0.0137	0.0052	0.0099	0.0028	0.0158	0.0096	0.0124	0.0164	0.0158	0.0075	0.0182	0.0176

Table 1. The overall recommendation performance of various methods across the three datasets (Overall) and the cold subsetresults (Cold). The best-performing and second-best methods in Overall are denoted with boldface and underlining, respectively. The “-” indicates that the item ID-based method cannot handle cold-start items, and we could treat its value as

0

. The improvements over the second-best methods are statistically significant (paired

t

-test,

p

-value

<0.05

Method	IID R.@10	N.@10	N.@20	R.@10	R.@20	Drop
Semantic ID-based
TIGER	0.7020	0.0108	0.0144	0.0215	0.0362	–
Cold-start-based
Retraining	0.6181	0.0080	0.0104	0.0165	0.0259	-25.9%
Finetuning	0.1052	0.0014	0.0018	0.0022	0.0037	-87.0%
SpecGR	0.3329	0.0067	0.0081	0.0207	0.0389	-38.0%

Table 2. Recommendation performance on the warm subset. Using the Phone dataset as an example. “IID R.” denotes IID Ratio; “N.” denotes NDCG; “R.” denotes RECALL; and “Drop” denotes the relative drop of N.@10 (%).

Model	IID R.@10	N.@10	N.@20	R.@10	R.@20
GenRecEdit	0.7359	0.0064	0.0078	0.0108	0.0165
Knowledge Preparation
w/o position-wise	0.0220	0.0002	0.0002	0.0004	0.0005
Layer Location
w/o classifier (random)	0.7339	0.0059	0.0072	0.0100	0.0150
w/o classifier (worst)	0.6865	0.0055	0.0066	0.0094	0.0137

Table 3. The results of the ablation study. Using the Phone dataset as an example. “w/o” indicates that the corresponding module is removed.

Figure 5. An analysis of the quality of constructed knowledge. The left panel shows performance under three settings on the cold subset, while the right panel reports the corresponding results on the overall test set.

Figure 6. A sensitivity analysis on the number of constructed knowledge. The left figure shows changes of three metrics on the cold subset (NDCG@10, NDCG@20, and IID Ratio@10), while the right figure reports metric variations on the overall test set (NDCG@10 and NDCG@20).

Figure 7. An analysis on the hyper parameter $\lambda$ . The left figure shows the performance trends on cold-start and warm items (NDCG@10), while the right reports the performance on the overall test set (NDCG@10 and NDCG@20).

Model Update	Retraining	Fintuning	SpecGR	GenRecEdit
Type	Train	Train	Alignment	Edit
Relative Time	100%	18.1%	41.6%	9.5%

Table 4. Comparison of update time across four algorithms.

Figure 8. Classfier Accuracy across layers. The left figure shows the result on Video, while the right figure reports the result on Phone.

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Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories. While prior models attempt to bridge this gap using item content features, they universally suffer from the seesaw dilemma: enhancing performance for cold items inevitably degrades performance for warm items, and vice versa. We identify that this dilemma stems from a fundamental distributional disparity: warm item embeddings occupy a complex behavioral manifold" shaped by rich interaction signals, whereas cold item embeddings are constrained to a semantic manifold" derived solely from auxiliary content. Existing methods often force a rigid mapping between these inconsistent spaces, causing the model to sacrifice the precision of warm representations to accommodate cold ones. To address this, we propose DiffCold, a diffusion-based generative model that unifies warm and cold representations. Unlike GANs or VAEs, DiffCold leverages conditional diffusion to reconstruct warm item embeddings from content, preserving the underlying manifold structure without degradation. We further tailor this paradigm with two specific designs: a Retrieval-enhanced Aggregator that initializes generation using semantically similar warm items to bypass inefficient noise, and a Simulation-based Representation Alignment module that enforces distribution consistency between generated and real embeddings via contrastive learning. Experiments on three benchmarks confirm that DiffCold resolves the seesaw dilemma, consistently outperforming state-of-the-art methods across all metrics.

Abstract

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