Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking
This work addresses the content gap in game recommendations for Roblox users, offering a scalable solution that is incremental by building on existing LLM capabilities.
The paper tackled the problem of inconsistent and sparse game text features in Roblox recommendations by using LLMs to generate structured attributes from in-game text and implementing a re-ranking mechanism, resulting in improved recommendation relevance and personalization, with deployment in production for applications like integrity detection.
With the vast and dynamic user-generated content on Roblox, creating effective game recommendations requires a deep understanding of game content. Traditional recommendation models struggle with the inconsistent and sparse nature of game text features such as titles and descriptions. Recent advancements in large language models (LLMs) offer opportunities to enhance recommendation systems by analyzing in-game text data. This paper addresses two challenges: generating high-quality, structured text features for games without extensive human annotation, and validating these features to ensure they improve recommendation relevance. We propose an approach that extracts in-game text and uses LLMs to infer attributes such as genre and gameplay objectives from raw player interactions. Additionally, we introduce an LLM-based re-ranking mechanism to assess the effectiveness of the generated text features, enhancing personalization and user satisfaction. Beyond recommendations, our approach supports applications such as user engagement-based integrity detection, already deployed in production. This scalable framework demonstrates the potential of in-game text understanding to improve recommendation quality on Roblox and adapt recommendations to its unique, user-generated ecosystem.