Collaborative Knowledge Fusion: A Novel Method for Multi-Task Recommender Systems via LLMs

Chuang Zhao, Xing Su, Ming He, Hongke Zhao, Jianping Fan, Xiaomeng Li

Abstract: Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based recommender systems primarily leverage item attributes and user interaction histories in textual format, improving the single task like rating prediction or explainable recommendation. Nevertheless, these approaches underestimate the crucial contribution of traditional collaborative signals in discerning users’ profound intentions and disregard the interrelatedness among tasks. To address these limitations, we introduce a novel framework known as CKF, specifically developed to boost multi-task recommendations via personalized collaborative knowledge fusion into LLMs. Specifically, to enhance collaborative signal integration, we develop a meta-network that creates personalized mapping bridges for each user. This enables the seamless incorporation of trained collaborative filtering embeddings into structured prompt templates, significantly boosting the LLM’s understanding of user interests. To investigate the intrinsic relationship among diverse recommendation tasks, we develop Multi-LoRA, a new parameter-efficient approach for multi-task optimization, adept at distinctly segregating task-shared and task-specific knowledge. This semantic approach forges a connection between LLMs and recommendation scenarios, while simultaneously enriching the supervisory signal through mutual knowledge transfer among various tasks. Extensive experiments and in-depth robustness analyses across four common recommendation tasks on four large public data sets substantiate our effectiveness.