the biggest issue is postgres not having tf-idf or BM25 style relevance algorithms.
that would have been the right fit for fuzzy name matching.
that would have been the right fit for fuzzy name matching.
https://www.alibabacloud.com/blog/keyword-analysis-with-post...
The key is you have to compute the IDF into a side table using the ts_stat function, since this is a global property (inter-document frequency) of your corpus. Once you have your IDF table you can write a ranking function based on that table.
the Alibaba Cloud article is nice..but we are really comparing this with Elastic
TF-IDF is a useful option as part of a high precision pipeline _post_ blocking.
A two step pipeline is typical and necessary because the high precision pipeline, while more accurate than the blocking algorithm, suffers from being slow.
Similaritiy search (https://www.elastic.co/guide/en/elasticsearch/reference/curr...) is far better assuming you have some corpus already.