User Behavior Retrieval for Click-Through Rate Prediction

Abstract

Click-through rate (CTR) prediction plays a key role in modern online personalization services. In practice, it is necessary to capture user's drifting interests by modeling sequential user behaviors to build an accurate CTR prediction model. However, as the users accumulate more and more behavioral data on the platforms, it becomes non-trivial for the sequential models to make use of the whole behavior history of each user. First, directly feeding the long behavior sequence will make online inference time and system load infeasible. Second, there is much noise in such long histories to fail the sequential model learning. The current industrial solutions mainly truncate the sequences and just feed recent behaviors to the prediction model, which leads to a problem that sequential patterns such as periodicity or long-term dependency are not embedded in the recent several behaviors but in far back history. To tackle these issues, in this paper we consider it from the data perspective instead of just designing more sophisticated yet complicated models and propose User Behavior Retrieval for CTR prediction (UBR4CTR) framework. In UBR4CTR, the most relevant and appropriate user behaviors will be firstly retrieved from the entire user history sequence using a learnable search method. These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones. It is highly feasible to deploy UBR4CTR into industrial model pipeline with low cost. Experiments on three real-world large-scale datasets demonstrate the superiority and efficacy of our proposed framework and models.

SIGIR 2020 industry track

References in corpus (6)

• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• Learning Tree-based Deep Model for Recommender Systems
• Collaborative Memory Network for Recommendation Systems
• Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
• Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling

Cited by in corpus (14)

• CL4CTR: A Contrastive Learning Framework for CTR Prediction
• Click-Through Rate Prediction with Multi-Modal Hypergraphs
• TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
• Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
• Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
• Exploring Scaling Laws of CTR Model for Online Performance Improvement
• FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
• Retrieval-Oriented Knowledge for Click-Through Rate Prediction
• Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction
• ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
• ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
• Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives
• LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction
• Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer