NxtPost: User to Post Recommendations in Facebook Groups

TL;DR

NxtPost is a Transformer-based recommender system for Facebook Groups that predicts user interests in posts, improving engagement and community interaction through personalized, sequential content recommendations.

Contribution

This paper introduces NxtPost, a novel Transformer-based model that handles large vocabularies and dynamic user preferences for improved post recommendations in Facebook Groups.

Findings

49% improvement in offline evaluation metrics

0.6% increase in users engaging with new people

Effective deployment strategies for cold start and freshness management

Abstract

In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups. Inspired by recent advances in NLP, we have adapted a Transformer-based model to the domain of sequential recommendation. We explore causal masked multi-head attention that optimizes both short and long-term user interests. From a user's past activities validated by defined safety process, NxtPost seeks to learn a representation for the user's dynamic content preference and to predict the next post user may be interested in. In contrast to previous Transformer-based methods, we do not assume that the recommendable posts have a fixed corpus. Accordingly, we use an external item/token embedding to extend a sequence-based approach to a large vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result of NxtPost deployment, 0.6% more users are meeting new people, engaging with the community, sharing knowledge and getting support. The paper shares our experience in developing a personalized sequential recommender system, lessons deploying the model for cold start users, how to deal with freshness, and tuning strategies to reach higher efficiency in online A/B experiments.