Enhancing ML Experimentation Speed at Etsy with Interleaving: Empowering Etsy’s Engineering Team
Introduction:
At Etsy, our product and machine learning teams are constantly working to improve the experience for our buyers and sellers. To ensure that our innovations are effective, we conduct experiments to validate their impact on user experience. While A/B testing is commonly used, we have also implemented interleaving, a lesser-known experimental design. Interleaving allows us to measure user preferences for ML models that produce ordered results, using only a fraction of the traffic needed for an equivalent A/B test. In this post, we will explain how interleaving works, how we implemented it at Etsy, and how we validate its performance. Interleaving offers a faster alternative to traditional A/B testing and allows us to experiment and iterate at a faster pace. However, it is not a replacement for A/B testing and has its limitations. Despite these limitations, our implementation of interleaving at Etsy has proven to be effective in measuring user preferences and improving the overall user experience.
Full Article: Enhancing ML Experimentation Speed at Etsy with Interleaving: Empowering Etsy’s Engineering Team
Interleaving: Accelerating Experimentation at Etsy
Etsy, a popular online marketplace, has been working tirelessly to enhance the experience of its users. To achieve this, the company’s product and machine learning teams are constantly developing new innovations. However, before these innovations can be implemented, they need to undergo thorough testing to ensure they deliver the desired improvements. While traditional A/B testing is widely used, Etsy has also experimented with a lesser-known method called interleaving. In this article, we will delve into how interleaving works, its benefits, and how Etsy has implemented it.
Measuring Algorithm Impact
When it comes to testing new algorithms or models built to produce search results, A/B testing is the go-to method. This involves randomly dividing visitors into two groups: a control group that experiences the old algorithm and a variant group that sees the new algorithm’s results. By comparing the average behaviors of the two groups, the impact of the new algorithm can be determined. If it proves to be significantly positive, the new algorithm is generally rolled out to all visitors.
Introducing Interleaving
However, Etsy wanted to dig deeper and understand individual preferences rather than relying solely on average behaviors. This is where interleaving comes into play. Unlike A/B testing, interleaving allows for the assessment of visitor preferences at an individual level. It accomplishes this by running search queries through both the old and new algorithms simultaneously, presenting ordered results from both to users. By tracking user engagement, such as purchases, Etsy can determine which algorithm users prefer.
Randomness and Variation Control
To ensure fair results, interleaving experiments require some randomness. Etsy uses a team-draft interleaving method, where each algorithm provides one listing for every pair of listings in the combined ranking. The order in which these listings are presented is randomized. If a listing from one algorithm has already been interleaved, it is skipped in favor of a new listing. This approach prevents preferential placement and ensures unbiased data collection.
The Speed Advantage
One of the main advantages of interleaving over A/B testing is its speed. A/B tests typically require a large sample size to yield statistically significant results. In contrast, interleaving experiments can achieve similar results with significantly less traffic. In fact, Etsy found that interleaving experiments require 10X to 100X less traffic than A/B tests. This time-saving benefit allows Etsy’s teams to iterate and experiment at a much faster pace.
Interleaving Implementation
Implementing interleaving at Etsy involved creating two components: the interleaver and the offline result calculator. The interleaver splits a user’s search request into two identical requests, which are processed by separate search pipelines. The results from both pipelines are then combined and returned to the user. The interleaver also tracks which algorithm produced each listing. The offline result calculator analyzes user actions and query data to establish preferences, attributing purchases to the respective algorithms.
Validating Interleaving
Before fully integrating interleaving into their system, Etsy conducted validation tests. They started with A-vs-A tests, where two identical copies of the control algorithm’s results were interleaved together. This ensured that the interleaver introduced no biases. Long-term A-vs-A tests were also conducted to verify the false positive rate and check for any seasonality effects. Performance concerns were addressed by embedding each interleaving test into an A/B test.
Conclusion
Interleaving has proven to be a valuable addition to Etsy’s experimentation process. By allowing for individual preference measurement and significantly reducing the required traffic, interleaving enables faster iteration and experimentation. While it has its limitations and cannot replace A/B testing for certain types of experiments, Etsy’s implementation of interleaving has yielded positive results, ultimately improving the experience for both buyers and sellers on their platform.
Summary: Enhancing ML Experimentation Speed at Etsy with Interleaving: Empowering Etsy’s Engineering Team
At Etsy, our product and machine learning (ML) teams are dedicated to improving the experience for our buyers and sellers. To validate their innovations, we conduct experiments with online traffic using A/B tests and a lesser-known experimental design called interleaving. Interleaving allows us to test ML models that produce ordered results faster and with less traffic than traditional A/B tests. In interleaving, search queries are run through both algorithms and the results are presented together, measuring user engagement to determine preferences. Interleaving is faster than A/B testing and enables us to experiment on models that would otherwise not have access to testing traffic. However, it is not a replacement for A/B testing and has limitations in terms of what it can test. To implement interleaving, we built an infrastructure consisting of an interleaver and an offline result calculator that split user search requests, collect and weave the results, and track user preferences. We validate the system’s performance, ensure it doesn’t introduce biases, and check for any impact on site performance. Overall, interleaving is a valuable tool for accelerating our experimentation process and improving user experiences.
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