Online Evaluation for Effective Web Service Development

09/03/2018 ∙ by Roman Budylin, et al. ∙ Yandex 0

Development of the majority of the leading web services and software products today is generally guided by data-driven decisions based on evaluation that ensures a steady stream of updates, both in terms of quality and quantity. Large internet companies use online evaluation on a day-to-day basis and at a large scale. The number of smaller companies using A/B testing in their development cycle is also growing. Web development across the board strongly depends on quality of experimentation platforms. In this tutorial, we overview state-of-the-art methods underlying everyday evaluation pipelines at some of the leading Internet companies. Software engineers, designers, analysts, service or product managers --- beginners, advanced specialists, and researchers --- can learn how to make web service development data-driven and do it effectively.



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Nowadays, the development of most leading web services and software products, in general, is guided by data-driven decisions that are based on online evaluation which qualifies and quantifies the steady stream of web service updates. Online evaluation is widely used in modern Internet companies (like search engines (Deng et al., 2014; Hohnhold et al., 2015; Drutsa et al., 2015b), social networks (Bakshy and Eckles, 2013; Xu and Chen, 2016), media providers (Bakshy and Eckles, 2013), and online retailers) in permanent manner and on a large scale. Yandex run more than 100 online evaluation experiments per day; Bing reported on more than 200 run A/B tests per day (Kohavi et al., 2013); and Google conducted more than 1000 experiments (Hohnhold et al., 2015). The number of smaller companies that use A/B testing in the development cycle of their products grows as well. The development of such services strongly depends on the quality of the experimentation platforms. In this tutorial, we overview the state-of-the-art methods underlying the everyday evaluation pipelines.

At the beginning of this tutorial (which is a shorter version of (Budylin et al., 2018a)), we make an introduction to online evaluation and give basic knowledge from mathematical statistics (40 min, Section 1). Then, we share approaches for development of online metrics (50 min, Section 2

). This is followed by rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics: emphasizing best practices and common pitfalls (55 min, Section 


). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently (65 min, Section 

4). Finally, we point out open research questions and current challenges that should be interesting for research scientists.

1. Statistical foundation

We introduce the main probabilistic terms, which form a theoretical foundation of A/B testing. We introduce the observed values as random variables sampled from an unknown distribution. Evaluation metrics are statistics based on observations (mean, median, quantiles, etc.). Overview of statistical hypothesis testing is provided with definitions of p-value, type I error, and type II error. We discuss several statistical tests (Student’s t-test, Mann Whitney U, and Bootstrap 

(Efron and Tibshirani, 1994)), compare their properties and applicability (Drutsa et al., 2015c).

2. Development of online metrics

We deeply discuss how to build evaluation metrics, what is the main ingredient in online experimentation pipeline. First, we introduce the notion of an A/B test (also known as an online controlled experiment): it compares two variants of a service at a time, usually its current version (control) and a new one (treatment), by exposing them to two groups of users (Peterson, 2004; Kohavi et al., 2007; Kohavi et al., 2009b). Main components of a metric are presented: key metric, evaluation statitistic, statistical significance test, Overall Evaluation Criterion (OEC) (Kohavi et al., 2009b), and Overall Acceptance Criterion (OAC) (Drutsa et al., 2015c). The aim of controlled experiments is to detect the causal effect of service updates on its performance relying on an Overall Evaluation Criterion (OEC) (Kohavi et al., 2009b), a user behavior metric (e.g., clicks-per-user, sessions-per-user, etc.) that is assumed to correlate with the quality of the service. We show that development of a new good metric is a challenging goal, since an appropriate OAC should possess two crucial qualities: directionality (the sign of the detected treatment effect should align with positive/negative impact of the treatment on user experience) and sensitivity (the ability to detect the statistically significant difference when the treatment effect exists) (Kohavi et al., 2012; Nikolaev et al., 2015; Poyarkov et al., 2016; Deng and Shi, 2016). The former property allows to make correct conclusions on the system quality changes (Kohavi et al., 2012; Nikolaev et al., 2015; Deng and Shi, 2016), while improvement of the latter one allows to detect metric changes in more experiments and to utilize less users (Kharitonov et al., 2015c, b; Poyarkov et al., 2016).

Second, we provide evaluation criteria beyond averages: (a) how to evaluate periodicity (Drutsa et al., 2017a; Drutsa, 2015; Drutsa et al., 2017b) and trends (Drutsa et al., 2015a; Drutsa, 2015; Drutsa et al., 2017a) of user behavior over days, e.g., for detection of delayed treatment effects; and (b) how to evaluate frequent/rare behavior and diversity in behavior between users that cannot be detected by mean values (Drutsa et al., 2015c). Third, product-based aspects in metric building are presented. Namely, we discuss vulnerability of metrics such as a click on a button to switch a search engine (Arkhipova et al., 2015) (i.e., how can a metric be gamed or manipulated); ways to measure different aspects of a service (i.e., speed (Kohavi et al., 2010; Kohavi et al., 2014), absence (Chakraborty et al., 2014), abandonment (Kohavi et al., 2014)); difference between metrics of user loyalty and ones of user activity (Rodden et al., 2010; Lalmas et al., 2014; Drutsa et al., 2015a, b; Drutsa, 2015; Drutsa et al., 2015c); dwell time to improve click-based metrics (Kim et al., 2014); how to evaluate the number of user tasks (which can have a complex hierarchy (Boldi et al., 2009)) by means of sessions (Song et al., 2013); and issues in session division (Jones and Klinkner, 2008) as well.

Fourth, math-based approaches to improve metrics are considered. In particular, we discuss the powerful method of linearization (Budylin et al., 2018b) that reduces any ratio metric to the average of a user-level metric preserving directionality and allowing usage of a wide range of sensitivity improvement techniques developed for user-level metrics. We also describe different methods of noise reduction (such as capping (Kohavi et al., 2014), slicing (Song et al., 2013; Deng and Hu, 2015), taking into account user activity, etc.) and of utilization of a user generated content approach.

Finally, some system requirements for metric building are discussed. We explain how to get a set of experiments with verdicts (known positiveness or negativeness), how to construct a pipeline to easily implement and test metrics, and how to measure metrics (Dmitriev and Wu, 2016).

3. Experimentation pipeline and workflow in the light of industrial practice

We share rich industrial experiences on constructing of an experimentation pipeline in large Internet companies. First, we discuss how can experiments be used for evaluation of changes in various components of web services: the user interface (Kohavi et al., 2009a; Drutsa et al., 2015a; Nikolaev et al., 2015; Drutsa et al., 2017a), ranking algorithms (Song et al., 2013; Drutsa et al., 2015a; Nikolaev et al., 2015; Drutsa et al., 2017a), sponsored search (Chawla et al., 2016), and mobile apps (Xu and Chen, 2016). Second, we consider several real cases of experiments, where pitfalls (Crook et al., 2009; Kohavi et al., 2012; Kohavi et al., 2014; Deng and Shi, 2016) are demonstrated and lessons are learned. In particular, we discuss: conflicting experiments, network effects (Gui et al., 2015), duration and seasonality (Shokouhi, 2011; Drutsa et al., 2015a), logging, and slices.

Third, we provide our management methodology to conduct experiments efficiently and to avoid the pitfalls. This methodology is based on pre-launch checklists and a team of Experts on Experiments (EE). We also present our system of tournaments, where problems similar to the ones in two-stage A/B testing (Deng et al., 2014) are solved. Finally, we discuss how large-scale experimental infrastructure (Tang et al., 2010; Kohavi et al., 2013; Xu et al., 2015) can be used to collect experiments for metric evaluation (Dmitriev and Wu, 2016).

4. Machine learning driven A/B testing

A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that improve the efficiency of online experiments. We start this section with the comparison of randomized experiments and observational studies. We explain that the difference between averages of the key metric may be misleading when measured in an observational study. We introduce the Neyman–Rubin model and rigorously formulate implicit assumptions we make each time when evaluating the results of randomized experiments.

Then we overview several studies devoted to the variance reduction of evaluation metrics. Regression adjustment techniques such as stratification, linear models 

(Deng et al., 2013; Xie and Aurisset, 2016)

, and gradient boosted decision trees 

(Poyarkov et al., 2016)

reduce the variance related to the observed features (covariates) of the users. We also consider experiments with user experience, where the effect of a service change is heterogeneous (is different for users of different types). We overview the main approaches to estimation of the heterogeneous treatment effect depending on the user features 

(Powers et al., 2017; Athey and Imbens, 2015).

We explain the Optimal Distribution Decomposition (ODD) approach that is based on the analysis of the control and treatment distributions of the key metric as a whole, and, for this reason, is sensitive to more ways the two distributions may actually differ 

(Nikolaev et al., 2015). Method of virtually increasing of the experiment duration through the prediction of the future (Drutsa et al., 2015b) is discussed. We also provide another way to improve sensitivity that is based on learning of metric combinations (Kharitonov et al., 2017). This approach showed outstanding sensitivity improvements in the large scale empirical evaluation (Kharitonov et al., 2017).

Finally, we discuss ways to improve the performance of experimentation pipeline as a whole. Optimal scheduling of online evaluation experiments is presented (Kharitonov et al., 2015b) and approaches for early stopping of them are highlighted (where inflation of Type I error (Johari et al., 2017) and ways to correctly make sequential testing (Kharitonov et al., 2015c; Deng et al., 2016) are discussed).

We also highlight important topics not covered by the tutorial: Bayesian approaches (Deng, 2015; Deng et al., 2016) and non-parametric mSRPT (Abhishek and Mannor, 2017) in sequential testing; network A/B testing (Gui et al., 2015; Saveski et al., 2017); two-stage A/B testing (Deng et al., 2014); Imperfect Treatment Assignment (Coey and Bailey, 2016); and interleaving (Joachims, 2002; Joachims et al., 2003; Radlinski et al., 2008; Hofmann et al., 2011; Chapelle et al., 2012; Radlinski and Craswell, 2013; Schuth et al., 2014; Kharitonov et al., 2015a; Aurisset et al., 2017; Radlinski and Yue, 2011; Grotov and de Rijke, 2016; Radlinski and Hofmann, 2013; Radlinski, 2013).

Tutorial materials

The tutorial materials (slides) are available at


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