Merging topic models

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Hi,

Thank you so much for this amazing package. This is a question and not an issue, I hope it's appropriate to post here.

In my use case, I re-train BERTopic daily to capture new topics that arise continuously. I tried online learning methods using the River library but the results were significantly worse than one-off training with UMAP and HDBSCAN, so I'm re-training for now. Naturally some of the topics that arise in a given day are similar or equivalent to topics from yesterday.

I have two questions regarding this. First, are there recommended ways to identify that a topic is similar to a topic from a previous topic model (using c-TF-IDF distribution, vector for centroid, etc)? And second, having identified similarities in topics between two topic models, is it possible to merge the models to get one large topic model incorporating both?

Thanks!

No files or tests are named. Start by reviewing BERTopic's model-training and topic-representation APIs, then compare them with the described River, UMAP, and HDBSCAN workflow. Define how topic similarity would be measured and what merging two trained models should produce; the issue is done only when both behaviors have a clear supported design.

Written by the indexing model from the issue text.