Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
It is common in the internet industry to use offline-developed algorithms to power online products that contribute to the success of a business. Offline-developed algorithms are guided by offline evaluation metrics, which are often different from online business key performance indicators (KPIs). To maximize business KPIs, it is important to pick a north star among all available offline evaluation metrics. By noting that online products can be measured by online evaluation metrics, the online counterparts of offline evaluation metrics, we decompose the problem into two parts. As the offline A/B test literature works out the first part: counterfactual estimators of offline evaluation metrics that move the same way as their online counterparts, we focus on the second part: causal effects of online evaluation metrics on business KPIs.
Debiasing Grid-based Product Search in E-commerce
The widespread usage of e-commerce websites in daily life and the resulting wealth of implicit feedback data form the foundation for systems that train and test e-commerce search ranking algorithms. We aim to utilize all types of implicit feedback as the supervision signals. In this work, we extend unbiased learning to rank to the world of e-commerce search via considering a grid-based product search scenario. We propose a novel framework which (1) forms the theoretical foundations to allow multiple types of implicit feedback in unbiased learning to rank and (2) incorporates the row skipping and slower decay click models to capture unique user behavior patterns in grid-based product search for inverse propensity scoring.
Interpretable Attribute-based Action-aware Bandits for Within-Session Personalization in E-commerce
When shopping online, buyers often express and refine their purchase preferences by exploring different items in the product catalog based on varying attributes, such as color, size, shape, and material. As such, it is increasingly important for e-commerce ranking systems to quickly learn a buyer’s fine-grained preferences and re- rank items based on their most recent activity within the session. In this paper, we propose an 𝑂nline 𝑃ersonalized 𝐴ttribute-based 𝑅e-ranker (OPAR), a light-weight, within-session personalization approach using multi-arm bandits (MAB). By representing each arm in the MAB as an attribute, we reduce the complexity space while offering more fine-grained personalization.
Personalization in E-commerce Product Search by User-Centric Ranking
E-commerce platforms offer the convenience of browsing through an entire catalog of inventory via a search bar. An unconventional inventory of unique products presents even greater challenges for product search, given that many of listings fall outside of standard e-commerce categories. With the potentially overwhelming number of relevant items per query, it becomes increasingly important for market places and platforms to help the user find items that best fit their preference and interest via a user-centric ranking model that generates personalized search results.
Attentive Sequential Models of Latent Intent for Next Item Recommendation
To discover such latent intents, and use them effectively for recommendation, in this paper we propose an Attentive Sequential model of Latent Intent (ASLI in short). Our model first learns item similarities from users' interaction histories via a self-attention layer, then uses a Temporal Convolutional Network layer to obtain a latent representation of the user's intent from her actions on a particular category. We use this representation to guide an attentive model to predict the next item.
The Difference Between a Click and a Cart-Add: Learning Interaction-Specific Embeddings
It all begins with an idea.
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce
In this work, we propose a novel next-item recommendation system which models a user's default, intrinsic preference, as well as two different kinds of occasion-based signals that may cause users to deviate from their normal behavior. More specifically, this model is novel in that it: (1) captures a personal occasion signal using an attention layer that models reoccurring occasions specific to that user (e.g. a birthday); (2) captures a global occasion signal using an attention layer that models seasonal or reoccurring occasions for many users (e.g. Christmas); (3) balances the user's intrinsic preferences with the personal and global occasion signals for different users at different times with a gating layer.
Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce
In this paper, we propose a novel revenue model which jointly optimizes both for probability of purchase and profit. The model is tested on a recommendation module at Etsy.com, a two-sided marketplace for buyers and sellers. Notably, optimizing for profit, in addition to purchase probability, benefits not only the platform but also the sellers. We show that the proposed model outperforms several baselines by increasing offline metrics associated with both relevance and profit.
Understanding the Role of Style in E-commerce Shopping
n this paper, we discuss a novel process by which we leverage 43 named styles given by merchandising experts in order to bootstrap large-scale style prediction and analysis of how style impacts purchase decision. We train a supervised, style-aware deep neural network that is shown to predict item style with high accuracy, while generating style-aware embeddings that can be used in downstream recommendation tasks. We share in our analysis, based on over a year's worth of transaction data and show that these findings are crucial to understanding how to more explicitly leverage style signal in industry-scale recommender systems.
The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis
In this paper, we introduce causal mediation analysis as a formal statistical tool to reveal the underlying causal mechanisms. Existing literature provides little guidance on cases where multiple unmeasured causally-dependent mediators exist, which are common in A/B tests. We seek a novel approach to identify in those scenarios direct and indirect effects of the treatment.
Learning Within-Session Budgets from Browsing Trajectories
In this paper, we find that users implicitly reveal their session-specific budgets through the sequence of items they browse within that session. Specifically, we find that some users “browse down,” by purchasing the cheapest item among alternatives under consideration, others “browse up” (selecting the most expensive), and others ultimately purchase items around the middle.