Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (1905.09248v3)

Abstract: Click-through rate (CTR) prediction is critical for industrial applications such as recommender system and online advertising. Practically, it plays an important role for CTR modeling in these applications by mining user interest from rich historical behavior data. Driven by the development of deep learning, deep CTR models with ingeniously designed architecture for user interest modeling have been proposed, bringing remarkable improvement of model performance over offline metric.However, great efforts are needed to deploy these complex models to online serving system for realtime inference, facing massive traffic request. Things turn to be more difficult when it comes to long sequential user behavior data, as the system latency and storage cost increase approximately linearly with the length of user behavior sequence. In this paper, we face directly the challenge of long sequential user behavior modeling and introduce our hands-on practice with the co-design of machine learning algorithm and online serving system for CTR prediction task. Theoretically, the co-design solution of UIC and MIMN enables us to handle the user interest modeling with unlimited length of sequential behavior data. Comparison between model performance and system efficiency proves the effectiveness of proposed solution. To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands. It now has been deployed in the display advertising system in Alibaba.

• The paper introduces a decoupled framework that separates real-time CTR inference from user interest computation via an asynchronous User Interest Center.
• The study proposes a novel Multi-channel Interest Memory Network that effectively handles user behavior sequences exceeding 1000 interactions.
• The research demonstrates significant improvements in prediction accuracy and latency reduction, enhancing scalability for large-scale recommender systems.

An Overview of Long Sequential User Behavior Modeling for Click-Through Rate Prediction

The paper "Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction" presents a meticulous paper on enhancing Click-Through Rate (CTR) prediction by leveraging long sequential user behavior data, a critical yet challenging task in industrial recommender systems and online advertising. The research, conducted by authors affiliated with the Alibaba Group, introduces a co-design approach combining advances in machine learning algorithms and system architecture to tackle inefficiencies and latency issues arising from long user sequences.

Key Contributions

The authors identify and address fundamental challenges in modeling long sequential user behavior data, particularly the exponential increase in system latency and storage requirements. The conventional methods, which struggle with sequences longer than 50 interactions, are significantly limited due to the linear rise in computational demand and storage costs as sequence length increases.

The proposed solution involves:

1. System Architecture Decoupling: They have innovatively decoupled the user interest computation from the real-time CTR prediction server by introducing a User Interest Center (UIC). The UIC maintains a constantly updated interest state for each user, based on real-time behavior events rather than traffic requests. This architecture effectively reduces real-time latency since the UIC operates asynchronously from the inference tasks, handling user interest separately and offline.
2. Machine Learning Innovation: A novel memory-based architecture, the Multi-channel user Interest Memory Network (MIMN), is proposed to efficiently handle sequences of any length. MIMN utilizes external memory to capture user interests progressively, which is essential for managing very long sequences. Enhanced with "memory utilization regularization" and "memory induction unit," MIMN ensures effective storage and efficient modeling of dynamic, evolving user interests.

Numerical Results and Implementation

The deployment of the UIC and MIMN provides a scalable solution for handling sequences well beyond 1000 interactions, a scale not previously feasible. Empirical results indicate significant improvements in system efficiency and CTR prediction accuracy, while maintaining constant latency irrespective of sequence length. These outcomes are crucial for handling the demands of large-scale systems like Alibaba’s advertising platform.

Implications and Future Work

The methodological advancements in this paper present substantial implications for the design and operation of industrial-scale recommender systems and advertising platforms. By separating the processing of user interest from real-time inference, it not only optimizes resource allocation but also enhances the real-time responsiveness of digital systems handling vast and complex datasets.

The proposed memory network architecture can be further explored in various contexts beyond CTR prediction, such as personalization systems and long-term trend modeling. Future research could establish greater generalization in various commercial and non-commercial applications, along with investigating more profound machine learning architectures that handle other forms of sequential data.

Overall, this work represents a strategic advancement in CTR prediction modeling by recognizing the potential of deep learning frameworks to accommodate extensive, rich historical behavior data, thereby driving forward the capabilities of real-time recommendation systems.