# FLEN: Leveraging Field for Scalable CTR Prediction Source: [FLEN: Leveraging Field for Scalable CTR Prediction]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:48.928Z Authors: Wenqiang Chen, Lizhang Zhan, Yuanlong Ci, Minghua Yang, Chen Lin, Dugang Liu Published: 2019-11-12T05:54:45.000Z ## Abstract Click-Through Rate (CTR) prediction has been an indispensable component for many industrial applications, such as recommendation systems and online advertising. CTR prediction systems are usually based on multi-field categorical features, i.e., every feature is categorical and belongs to one and only one field. Modeling feature conjunctions is crucial for CTR prediction accuracy. However, it requires a massive number of parameters to explicitly model all feature conjunctions, which is not scalable for real-world production systems. In this paper, we describe a novel Field-Leveraged Embedding Network (FLEN) which has been deployed in the commercial recommender system in Meitu and serves the main traffic. FLEN devises a field-wise bi-interaction pooling technique. By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications. We show that a variety of state-of-the-art CTR models can be expressed under this technique. Furthermore, we develop Dicefactor: a dropout technique to prevent independent latent features from co-adapting. Extensive experiments, including offline evaluations and online A/B testing on real production systems, demonstrate the effectiveness and efficiency of FLEN against the state-of-the-arts. Notably, FLEN has obtained 5.19% improvement on CTR with 1/6 of memory usage and computation time, compared to last version (i.e. NFM). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [CTR Prediction on Meitu (test)](): CTR Prediction; Meitu (test); AUC: 0.6475. - [Click-Through Rate Prediction on Avazu (test)](): Click-Through Rate Prediction; Avazu (test); AUC: 0.7528. - [CTR Prediction on Avazu](): CTR Prediction; Avazu; AUC: 75.19. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()