GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
In recent years, generative recommendation (GR) has emerged as a promising strategy for sequential recommendation tasks, leveraging an end-to-end generation paradigm. However, a significant challenge persists in the form of cold-start collapse; traditional GR models experience a dramatic decline in recommendation accuracy for cold-start items, often nearing zero. This issue poses a considerable barrier to achieving effective recommendations in rapidly evolving catalogs, where new items frequently appear.
Current approaches to mitigate cold-start issues typically involve retraining models using newly acquired data from cold-start interactions. Unfortunately, this method is fraught with limitations, including:
- Sparse feedback, which complicates the learning process.
- High computational costs associated with model retraining.
- Delayed updates that can hinder timely recommendations, particularly in dynamic environments.
In light of these challenges, researchers have turned towards model editing techniques borrowed from natural language processing (NLP). These techniques facilitate the injection of knowledge into large language models without the need for extensive retraining. The application of such methods to generative recommendation, however, is not straightforward, primarily due to two significant hurdles:
- Lack of explicit subject-object binding: Unlike natural language, generative recommendation models do not inherently represent relationships between items in a structured manner, making targeted edits more complex.
- Unstable token co-occurrence patterns: Generative recommendation does not maintain consistent relationships between multiple tokens, which complicates the reliable injection of multi-token item representations.
To address these challenges, a novel framework known as GenRecEdit has been introduced. This model editing framework is specifically designed for generative recommendation and employs several innovative strategies:
- Explicit context modeling: GenRecEdit establishes a clear relationship between the full sequence context and next-token generation, allowing for more targeted modifications.
- Iterative token-level editing: This approach facilitates the injection of multi-token item representations, enhancing the model’s ability to understand and recommend cold-start items effectively.
- One-to-one trigger mechanism: By implementing a mechanism to minimize the interference among multiple edits during inference, GenRecEdit ensures that the integrity of the model’s original recommendations remains preserved.
Extensive experiments conducted across various datasets have demonstrated the effectiveness of GenRecEdit. The results indicate a substantial improvement in recommendation performance specifically for cold-start items, while simultaneously maintaining the original quality of recommendations. Notably, GenRecEdit achieves these enhancements with only about 9.5% of the training time required for traditional retraining methods, underscoring its efficiency and practicality for frequent model updates.
As the landscape of generative recommendation continues to evolve, frameworks like GenRecEdit are poised to play a crucial role in overcoming the cold-start challenge, paving the way for more responsive and effective recommendation systems. Researchers and practitioners alike are optimistic that this innovative approach will lead to significant advancements in the field, enhancing user experience and satisfaction in various applications.
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