BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations

TL;DR

BoostJet combines statistical aggregates of user and offer features with neural embeddings of offer sequences, using gradient boosted decision trees to enhance recommendation quality and scalability in large-scale e-commerce settings.

Contribution

The paper introduces Trackers for capturing diverse features and Offer2Vec for offer relations, integrating them with neural embeddings in BoostJet for improved recommendations.

Findings

BoostJet significantly outperforms baseline recommenders in quality.

It demonstrates high scalability on large datasets.

The approach effectively captures complex user and offer interactions.

Abstract

Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendations. However, the quality of the generated recommendations depends on user features (like demography, temporality), offer features (like popularity, price), and user-offer features (like implicit or explicit feedback). Current state-of-the-art recommenders do not explore such diverse features concurrently while generating the recommendations. In this paper, we first introduce the notion of Trackers which enables us to capture the above-mentioned features and thus incorporate users' online behaviour through statistical aggregates of different features (demography, temporality, popularity, price). We also show how to capture offer-to-offer relations, based on their consumption sequence, leveraging neural embeddings for offers in our Offer2Vec algorithm. We then introduce BoostJet, a novel recommender which integrates the Trackers along with the neural embeddings using MatrixNet, an efficient distributed implementation of gradient boosted decision tree, to improve the recommendation quality significantly. We provide an in-depth evaluation of BoostJet on Yandex's dataset, collecting online behaviour from tens of millions of online users, to demonstrate the practicality of BoostJet in terms of recommendation quality as well as scalability.