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Recommenders have become widely popular in recent years because of theirbroader applicability in many e-commerce applications. These applications relyon recommenders for generating advertisements for various offers or providingcontent recommendations. However, the quality of the generated recommendationsdepends on user features (like demography, temporality), offer features (likepopularity, price), and user-offer features (like implicit or explicitfeedback). Current state-of-the-art recommenders do not explore such diversefeatures concurrently while generating the recommendations.  In this paper, we first introduce the notion of Trackers which enables us tocapture the above-mentioned features and thus incorporate users' onlinebehaviour through statistical aggregates of different features (demography,temporality, popularity, price). We also show how to capture offer-to-offerrelations, based on their consumption sequence, leveraging neural embeddingsfor offers in our Offer2Vec algorithm. We then introduce BoostJet, a novelrecommender which integrates the Trackers along with the neural embeddingsusing MatrixNet, an efficient distributed implementation of gradient boosteddecision tree, to improve the recommendation quality significantly. We providean in-depth evaluation of BoostJet on Yandex's dataset, collecting onlinebehaviour from tens of millions of online users, to demonstrate thepracticality of BoostJet in terms of recommendation quality as well asscalability.

BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations