IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval
This addresses the problem of real-time adaptation to changing user preferences for large-scale industrial platforms, representing an incremental improvement in multi-interest retrieval methods.
The paper tackles the challenge of dynamic personalized retrieval and recommendation in online community platforms by proposing the IRA framework, which adapts to evolving user interests through cumulative Interest Units and semantic-based retrieval, achieving robust personalization as validated on real-world datasets including NAVER's CAFE.
Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform.