Deep Pattern Network for Click-Through Rate Prediction.

Click-through rate (CTR) prediction tasks play a pivotal role in real-worldapplications, particularly in recommendation systems and online advertising. Asignificant research branch in this domain focuses on user behavior modeling.Current research predominantly centers on modeling co-occurrence relationshipsbetween the target item and items previously interacted with by users in theirhistorical data. However, this focus neglects the intricate modeling of userbehavior patterns. In reality, the abundance of user interaction recordsencompasses diverse behavior patterns, indicative of a spectrum of habitualparadigms. These patterns harbor substantial potential to significantly enhanceCTR prediction performance. To harness the informational potential within userbehavior patterns, we extend Target Attention (TA) to Target Pattern Attention(TPA) to model pattern-level dependencies. Furthermore, three criticalchallenges demand attention: the inclusion of unrelated items within behaviorpatterns, data sparsity in behavior patterns, and computational complexityarising from numerous patterns. To address these challenges, we introduce theDeep Pattern Network (DPN), designed to comprehensively leverage informationfrom user behavior patterns. DPN efficiently retrieves target-related userbehavior patterns using a target-aware attention mechanism. Additionally, itcontributes to refining user behavior patterns through a pre-training paradigmbased on self-supervised learning while promoting dependency learning withinsparse patterns. Our comprehensive experiments, conducted across three publicdatasets, substantiate the superior performance and broad compatibility of DPN.