Bridge the Unseen Gap: Enhancing Non-overlapping Cross-domain CTR Prediction via Profile Retrieval

Abstract

Click-through rate (CTR) prediction is a fundamental task in industrial recommender systems. Cross-domain CTR prediction, which leverages data from a source domain to improve performance in a target domain, has emerged as a key strategy. However, most existing methods rely on overlapping users or items across domains to enable knowledge transfer, which fails in prevalent real-world scenarios where domains are functionally or geographically isolated (e.g., cross-country services). In this paper, we introduce a novel paradigm shift, from implicit representation alignment to explicit retrieval-based instance transfer. We propose LLM-PRIT, a framework for Large Language Model-generated Profile Retrieval & Instance Transfer. Our framework operates in three cohesive stages. First, it utilizes an LLM as a universal semantic interpreter to generate domain-agnostic, transferable profiles for users and items, encapsulating open-world knowledge. Second, instead of directly using these textual profiles, it employs them as semantic anchors to retrieve the most relevant historical instances from the source domain. This step explicitly establishes cross-domain correlations while avoiding the modality gap. Finally, it transfers knowledge by efficiently fine-tuning the target CTR model on the retrieved instances, preserving the model’s inherent feature-interaction capabilities. We conduct extensive experiments on a public and a real-world industrial dataset. Both online and offline results demonstrate the effectiveness of our LLM-PRIT, bridging the unseen gap with open-world semantic information.