CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs
Yi Sun, Yuri M. Brovman

TL;DR
CoActionGraphRec is a novel deep learning model for e-commerce recommendations that leverages co-action graphs to better capture user interests and item relationships, addressing data sparsity challenges.
Contribution
The paper introduces CoActionGraphRec, a two-tower deep learning framework utilizing co-action graphs and graph neural networks tailored for eBay's sparse data environment.
Findings
Improved recommendation accuracy over state-of-the-art methods.
Effective modeling of user behavior sequences with graph-based representations.
Successful deployment with positive online A/B test results.
Abstract
There are unique challenges to developing item recommender systems for e-commerce platforms like eBay due to sparse data and diverse user interests. While rich user-item interactions are important, eBay's data sparsity exceeds other e-commerce sites by an order of magnitude. To address this challenge, we propose CoActionGraphRec (CAGR), a text based two-tower deep learning model (Item Tower and User Tower) utilizing co-action graph layers. In order to enhance user and item representations, a graph-based solution tailored to eBay's environment is utilized. For the Item Tower, we represent each item using its co-action items to capture collaborative signals in a co-action graph that is fully leveraged by the graph neural network component. For the User Tower, we build a fully connected graph of each user's behavior sequence, with edges encoding pairwise relationships. Furthermore, an…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Recommender Systems and Techniques · Topic Modeling
MethodsGraph Neural Network
