CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs

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 explicit interaction module learns representations capturing behavior interactions. Extensive offline and online A/B test experiments demonstrate the effectiveness of our proposed approach and results show improved performance over state-of-the-art methods on key metrics.