Charles Rosenberg

Nikil Pancha, Andrew Zhai, Jure Leskovec et al.

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While theoretically simplistic, these models are quite challenging to deploy in production, commonly requiring streaming infrastructure to reflect the latest user activity and potentially managing mutable data for encoding a user's hidden state. Here we introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement using a sequential model of a user's recent actions. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss, modeling long-term future actions instead of next action prediction. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action. We describe our design decisions via extensive offline experimentation and ablations and validate the efficacy of our approach in A/B experiments showing substantial improvements in Pinterest's user retention and engagement when comparing PinnerFormer against our previous user representation. PinnerFormer is deployed in production as of Fall 2021.

Paul Baltescu, Haoyu Chen, Nikil Pancha et al.

Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases including user, image and search based recommendations. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost. While most prior work focuses on building product embeddings from features coming from a single modality, we introduce a transformer-based architecture capable of aggregating information from both text and image modalities and show that it significantly outperforms single modality baselines. We also utilize multi-task learning to make ItemSage optimized for several engagement types, leading to a candidate generation system that is efficient for all of the engagement objectives of the end-to-end recommendation system. Extensive offline experiments are conducted to illustrate the effectiveness of our approach and results from online A/B experiments show substantial gains in key business metrics (up to +7% gross merchandise value/user and +11% click volume).

Saket Gurukar, Nikil Pancha, Andrew Zhai et al.

Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation and search. At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph. The Pin-Board graph contains pin and board entities and the graph captures the pin belongs to a board interaction. However, there exist several entities at Pinterest such as users, idea pins, creators, and there exist heterogeneous interactions among these entities such as add-to-cart, follow, long-click. In this work, we show that training deep learning models on graphs that captures these diverse interactions would result in learning higher-quality pin embeddings than training PinSage on only the Pin-Board graph. To that end, we model the diverse entities and their diverse interactions through multiple bipartite graphs and propose a novel data-efficient MultiBiSage model. MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings. We take this pragmatic approach as it allows us to utilize the existing infrastructure developed at Pinterest -- such as Pixie system that can perform optimized random-walks on billion node graphs, along with existing training and deployment workflows. We train MultiBiSage on six bipartite graphs including our Pin-Board graph. Our offline metrics show that MultiBiSage significantly outperforms the deployed latest version of PinSage on multiple user engagement metrics.

Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh et al. · stanford

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.