Online community platforms require dynamic personalized retrieval and
recommendation that can continuously adapt to evolving user interests and new
documents. Jedoch, optimizing models to handle such changes in real-time
remains a major challenge in large-scale industrial settings. Um dies anzugehen,
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, Und (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.

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