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Bringing Model Editing to Generative Recommendation in Cold-Start Scenarios

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

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.

Bringing Model Editing to Generative Recommendation in Cold-Start Scenarios

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.
Paper Structure (30 sections, 13 equations, 8 figures, 4 tables)

This paper contains 30 sections, 13 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Generative Recommendation
  4. Model Editing
  5. Problem Analysis and Formulation
  6. Problem Analysis
  7. Model Editing in GR
  8. Information Flow in FFN
  9. Editing Targets
  10. Preliminary in GR
  11. GenRecEdit: The Proposed Approach
  12. Position-Wise Knowledge Preparation
  13. Locate-Then-Edit Framework
  14. Layer Location
  15. Memory Construction
  16. ...and 15 more sections

Figures (8)

  • 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.
  • 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.
  • ...and 3 more figures