NxtPost: User to Post Recommendations in Facebook Groups
Kaushik Rangadurai, Yiqun Liu, Siddarth Malreddy, Xiaoyi Liu, Piyush, Maheshwari, Vishwanath Sangale, Fedor Borisyuk

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…
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling · Expert finding and Q&A systems
MethodsSoftmax · Linear Layer
