On the Practice of Scaling Search Conversion Rate Prediction
Improved search conversion rate prediction by scaling model backbone, embedding parameters, and training data, achieving a +2.6% increase.
Key Findings
Methodology
This paper conducts systematic experiments to explore the scaling mechanisms of search conversion rate prediction models. The focus is on scaling model backbone, embedding parameters, and data volume. Using over a year of customer interaction logs from a high-traffic e-commerce platform, the scalability of various state-of-the-art architectures and their ensembles was evaluated. Inference optimization strategies like decoupled graph execution and dynamic batching enabled low-latency GPU serving for high-capacity models.
Key Results
- Compared to a pre-scaling production model baseline, the deployed model achieved a +2.6% increase in search conversion rate with 2.5x larger training data and 8x more inference compute while having minimal latency impact.
- Experiments show selecting the right backbone and scaling factors is crucial; the impact of scaling backbone, embedding, and data is largely independent and additive.
- A streamlined warmstart strategy accelerates training iterations and simplifies new updates.
Significance
This research holds significant implications for both academia and industry. By systematically exploring the feasibility of model scaling, it provides generalizable guidance for scaling models in production settings. The findings suggest that the impact of scaling backbone, embedding, and data is largely independent and additive, offering possibilities for more efficient scaling exploration. Inference optimization strategies achieved low-latency GPU serving for high-capacity models, significantly improving search conversion rates.
Technical Contribution
The technical contributions include proposing a general and robust scaling mechanism for search conversion rate prediction models. Systematic experiments validated the scalability of various state-of-the-art architectures and their ensembles. The proposed streamlined warm-starting technique significantly reduces the time required for routine model development in production. A practical framework for optimizing GPU efficiency supports the deployment of large-scale CVR models.
Novelty
This paper is the first to systematically study the scaling mechanisms of search conversion rate prediction models. Unlike previous studies, it not only focuses on single-factor scaling but also explores comprehensive strategies for scaling backbone, embedding, and data. The experiments verify their independence and additivity, providing new perspectives for model scaling.
Limitations
- The method may encounter memory bottlenecks when handling very high-dimensional embeddings, especially in deep sequence models.
- Although inference optimization strategies are effective, further optimization may be needed in extremely high-traffic scenarios.
Future Work
Future research directions include further optimizing the inference process to handle higher traffic scenarios and exploring more strategies for scaling backbone and embedding. Additionally, research is needed on reducing memory consumption without affecting performance.
AI Executive Summary
Search conversion rate prediction models are crucial in e-commerce platforms, but scaling these models in high-traffic environments is challenging. Existing methods often fail to balance model quality with training cost and serving latency.
This paper proposes an effective approach to scaling modern search conversion rate prediction models. Through systematic experiments, the impact of scaling model backbone, embedding parameters, and data volume on model quality was studied. The results show that selecting the right backbone and scaling factors is crucial, and the impact of scaling backbone, embedding, and data is largely independent and additive.
A streamlined warmstart strategy and inference optimization strategies enabled low-latency GPU serving for high-capacity models. The deployed model achieved a +2.6% increase in search conversion rate with 2.5x larger training data and 8x more inference compute while having minimal latency impact.
Deep Analysis
Background
With the rapid development of e-commerce platforms, search conversion rate prediction models play a vital role in recommendation systems. In recent years, deep learning technologies have significantly improved model quality, but effectively scaling these models to handle large-scale data remains a challenge. Many studies show that the incremental benefits of model scaling often diminish rapidly, especially in the search domain.
Core Problem
The core problem is how to scale search conversion rate prediction models in high-traffic environments while maintaining high quality and low latency. The heterogeneity of models and complex input features make scaling effects inconsistent across different components, increasing the difficulty of scaling.
Innovation
The core innovation of this paper is proposing a general and robust scaling mechanism for search conversion rate prediction models. Systematic experiments validated the scalability of various state-of-the-art architectures and their ensembles. The proposed streamlined warm-starting technique significantly reduces the time required for routine model development in production.
Methodology
- �� Used a large-scale production dataset to evaluate the scalability of various state-of-the-art architectures and their ensembles.
- �� The impact of scaling backbone, embedding, and data is largely independent and additive.
- �� A streamlined warmstart strategy accelerates training iterations and simplifies new updates.
- �� Inference optimization strategies like decoupled graph execution and dynamic batching enabled low-latency GPU serving for high-capacity models.
Experiments
Experiments used over a year of customer interaction logs to evaluate the scalability of various state-of-the-art architectures and their ensembles. Online A/B tests validated the model's effectiveness in real-world applications. The deployed model achieved a +2.6% increase in search conversion rate with 2.5x larger training data and 8x more inference compute while having minimal latency impact.
Results
Experiments show selecting the right backbone and scaling factors is crucial; the impact of scaling backbone, embedding, and data is largely independent and additive. A streamlined warmstart strategy accelerates training iterations and simplifies new updates.
Applications
The applications of this research include search and recommendation systems on e-commerce platforms. By optimizing model scaling strategies, search conversion rates can be improved without increasing latency, enhancing user experience and platform revenue.
Limitations & Outlook
Although inference optimization strategies are effective, further optimization may be needed in extremely high-traffic scenarios. Additionally, the method may encounter memory bottlenecks when handling very high-dimensional embeddings, especially in deep sequence models.
Plain Language Accessible to non-experts
Imagine you're shopping in a large supermarket. To find what you want, you need an efficient search system. This system needs to be fast and accurately recommend items you might be interested in. The models studied in this paper are like the supermarket's search engine, enhancing its computing power and data volume to remain efficient when handling numerous customer queries. By optimizing the model's structure and parameters, researchers successfully improved search accuracy and speed, just like adding more shelves and product information in the supermarket to make it easier for you to find what you want.
ELI14 Explained like you're 14
Imagine you're playing a massive multiplayer online game. There are many players and items, and you need a fast system to find what you want. This system is like the game's search engine, helping you quickly find the gear and quests you need. Researchers increased the system's computing power and data volume, allowing this search engine to remain fast and accurate when handling many player queries. It's like adding more servers and data in the game to make your experience smoother.
Glossary
CVR (Conversion Rate)
Conversion rate refers to the proportion of users who complete a goal after performing an action.
In this paper, CVR measures the probability of a user making a purchase after a search.
Embedding
Embedding is a technique for mapping high-dimensional data into a lower-dimensional space for easier processing and analysis.
In this paper, embeddings represent user and item features.
Backbone
The backbone is the core structure of a model responsible for feature interaction and representation.
This paper studies the impact of different backbone structures on model scaling.
Warmstart
Warmstart is a method for accelerating model training by utilizing previous training results to reduce training time.
The proposed warmstart strategy significantly accelerates training iterations.
Dynamic Batching
Dynamic batching is a method for optimizing the inference process by dynamically adjusting batch sizes to improve computational efficiency.
Dynamic batching is used to achieve low-latency GPU serving for high-capacity models.
Open Questions Unanswered questions from this research
- 1 How to reduce memory consumption without affecting performance, especially when handling high-dimensional embeddings.
- 2 How to further optimize the inference process to maintain low latency in extremely high-traffic scenarios.
Applications
Immediate Applications
E-commerce Platform Optimization
Improve user experience and platform revenue by enhancing search conversion rate prediction models.
Long-term Vision
Intelligent Recommendation Systems
Apply optimized models to more fields, such as ad recommendations and personalized content delivery.
Abstract
Scaling a Search Conversion Rate (CVR) prediction model, especially in high-traffic environments, presents a challenge: superior model quality needs to be balanced with strict constraints on training cost and serving latency. This paper details an effective approach for scaling modern search CVR prediction models. We begin with an empirical study to understand the scaling performance of search CVR models, analyzing how quality improves as we scale three key factors of model backbone computation, the size of embedding parameters, and the volume of training data. We use a large-scale production dataset, comprising over a year of customer interaction logs from a high-traffic e-commerce platform, to evaluate the scalability of several state-of-the-art architectures and their ensembles. Our key findings are: (1) selecting the right backbone and scaling factors is crucial; (2) the impact of scaling backbone, embedding, and data is largely independent and additive, which has implications for more efficient scaling exploration; (3) a streamlined warmstart strategy can accelerate training iterations while simplifying new updates; (4) inference optimization strategies such as decoupled graph execution and dynamic batching can enable low-latency GPU serving even for high-capacity models. Compared to a baseline of a pre-scaling production model, we ultimately deployed a model trained on 2.5x larger training data with 8x more inference compute while having minimal latency impact. Online A/B tests also demonstrate that our launches achieved a combined +2.6% gain in a key metric of search conversion rate.