Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data

Click-through rate (CTR) prediction plays an indispensable role in online recommendation and advertising platforms. Numerous deep learning based models have been proposed to improve CTR prediction accuracy, and they typically leverage user behavior sequences to capture users' shifting preferences. However, these historical sequences of user interactions often suffer from severe homogeneity and scarcity compared to the extensive item pool. Relying solely on such sequences for user representations is inherently restrictive, as user interests extend beyond the scope of items they have previously engaged with. To address this challenge, we propose a data-driven approach to enrich user representations.We recognize user profiling and recall items as two ideal data sources within the cross-stage framework, encompassing the u2u (user-to-user) and i2i (item-to-item) aspects, respectively, because of their higher relevance to target users and ranking items, as well as their greater diversity. In this paper, we propose a novel architecture named Recall-Augmented Ranking (RAR).RAR consists of two key sub-modules, namely the Cross-Stage User and Item Selection Module and the Co-Interaction Module. These sub-modules synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations. Notably, RAR is orthogonal to many existing CTR models, allowing for seamless integration and consistent performance improvements in a plug-and-play manner. Extensive experiments are conducted on CTR prediction benchmarks, which verify the efficacy and compatibility of RAR against state-of-the-art methods.