UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction

TL;DR

UFIN introduces a universal feature interaction network leveraging textual data and large language models to improve multi-domain CTR prediction, enabling better transferability and knowledge sharing across diverse recommendation scenarios.

Contribution

The paper proposes a novel multi-modal encoder-decoder framework and a mixture-of-experts model to learn transferable feature interactions across domains, overcoming limitations of ID-based models.

Findings

UFIN outperforms existing models on eight datasets in multi-domain settings.

The approach effectively bridges semantic gaps between domains.

UFIN demonstrates strong cross-platform generalization capabilities.

Abstract

Click-Through Rate (CTR) prediction, which aims to estimate the probability of a user clicking on an item, is a key task in online advertising. Numerous existing CTR models concentrate on modeling the feature interactions within a solitary domain, thereby rendering them inadequate for fulfilling the requisites of multi-domain recommendations in real industrial scenarios. Some recent approaches propose intricate architectures to enhance knowledge sharing and augment model training across multiple domains. However, these approaches encounter difficulties when being transferred to new recommendation domains, owing to their reliance on the modeling of ID features (e.g., item id). To address the above issue, we propose the Universal Feature Interaction Network (UFIN) approach for CTR prediction. UFIN exploits textual data to learn universal feature interactions that can be effectively transferred across diverse domains. For learning universal feature representations, we regard the text and feature as two different modalities and propose an encoder-decoder network founded on a Large Language Model (LLM) to enforce the transfer of data from the text modality to the feature modality. Building upon the above foundation, we further develop a mixtureof-experts (MoE) enhanced adaptive feature interaction model to learn transferable collaborative patterns across multiple domains. Furthermore, we propose a multi-domain knowledge distillation framework to enhance feature interaction learning. Based on the above methods, UFIN can effectively bridge the semantic gap to learn common knowledge across various domains, surpassing the constraints of ID-based models. Extensive experiments conducted on eight datasets show the effectiveness of UFIN, in both multidomain and cross-platform settings. Our code is available at https://github.com/RUCAIBox/UFIN.