Efficient Large-Scale Vision Representation Learning

In this article, we present our approach to single-modality vision representation learning. Understanding vision representations of product content is vital for recommendations, search, and advertising applications in e-commerce. We detail and contrast techniques used to fine tune large-scale vision representation learning models in an efficient manner under low-resource settings, including several pretrained backbone architectures, both in the convolutional neural network as well as the vision transformer family. We highlight the challenges for e-commerce applications at-scale and highlight the efforts to more efficiently train, evaluate, and serve visual representations. We present ablation studies for several downstream tasks, including our visually similar ad recommendations. We evaluate the offline performance of the derived visual representations in downstream tasks. To this end, we present a novel text-to-image generative offline evaluation method for visually similar recommendation systems. Finally, we include online results from deployed machine learning systems in production at Etsy.

adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads

In this article, we present our approach to personalizing Etsy Ads through encoding and learning from short-term (one-hour) sequences of user actions and diverse representations. To this end we introduce a three-component adSformer diversifiable personalization module (ADPM) and illustrate how we use this module to derive a short-term dynamic user representation and personalize the Click-Through Rate (CTR) and Post-Click Conversion Rate (PCCVR) models used in sponsored search (ad) ranking. The first component of the ADPM is a custom transformer encoder that learns the inherent structure from the sequence of actions. ADPM's second component enriches the signal through visual, multimodal and textual pretrained representations. Lastly, the third ADPM component includes a "learned" on the fly average pooled representation. The ADPM-personalized CTR and PCCVR models, henceforth referred to as adSformer CTR and adSformer PCCVR, outperform the CTR and PCCVR production baselines by $+6.65\%$ and $+12.70\%$, respectively, in offline Precision-Recall Area Under the Curve (PR AUC). At the time of this writing, following the online gains in A/B tests, such as $+5.34\%$ in return on ad spend, a seller success metric, we are ramping up the adSformers to $100\%$ traffic in Etsy Ads.

Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time

Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure. In this work, we develop a new framework to solve any combinatorial optimization problem over graphs that can be formulated as a single player game defined by states, actions, and rewards, including minimum spanning tree, shortest paths, traveling salesman problem, and vehicle routing problem, without expert knowledge. Our method trains a graph neural network using reinforcement learning on an unlabeled training set of graphs. The trained network then outputs approximate solutions to new graph instances in linear running time. In contrast, previous approximation algorithms or heuristics tailored to NP-hard problems on graphs generally have at least quadratic running time. We demonstrate the applicability of our approach on both polynomial and NP-hard problems with optimality gaps close to 1, and show that our method is able to generalize well: (i) from training on small graphs to testing on large graphs; (ii) from training on random graphs of one type to testing on random graphs of another type; and (iii) from training on random graphs to running on real world graphs.