Collaborative User Prompt for Personalized Generative Recommendation
This work addresses the challenge of incorporating collaborative signals in generative recommender systems for improved personalization, representing an incremental advancement in the field.
The paper tackles the problem that existing generative recommendation methods overlook collaborative signals among similar users by proposing a compositional framework that integrates individual and collective preferences to build personalized soft prompts, achieving effectiveness across sequential recommendation, top-n recommendation, and explanation generation tasks on three real-world datasets.
Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generative recommendation methods often rely on discrete ID-based prompts or task-specific soft prompts, which overlook the valuable collaborative signals shared among users with similar interests. To address this limitation, this paper presents a compositional framework that integrates a user's individual preferences with collective preferences from similar users to build personalized soft prompts. Specifically, an attention-based mechanism fuses embeddings from users with similar interests, creating a richer representation that captures multiple facets of user preferences. This design dynamically emphasizes shared interests while preserving individual user preferences. Experiments on three real-world datasets demonstrate the effectiveness of the proposed approach across sequential recommendation, top-n recommendation, and explanation generation tasks, underscoring the advantages of incorporating collaborative signals through an attention-based compositional strategy.