BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations
This work addresses the need for more effective and scalable recommenders in e-commerce applications by integrating statistical aggregates and neural embeddings, though it appears incremental as it builds on existing techniques like gradient boosted decision trees.
The paper tackles the problem of improving recommendation quality by concurrently incorporating diverse user, offer, and interaction features, which current state-of-the-art methods often neglect, and demonstrates that BoostJet significantly enhances recommendation quality on a large-scale Yandex dataset with tens of millions of users.
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.