These clusters of similar embeddings correspond to similar records. So for a target record, we identify its cluster, determined by hash values, and all records in that cluster are considered the most closely related candidates.
Now that we have a flexible, embedding-based method for finding similar candidates, we must now find the true matches of our target profile amongst the top candidates. To do so, we developed a general matching formula, a combination of edit distances and metaphone (word pronunciation) distances, to produce a matching confidence score for any pair of strings, from any features.
Conclusion
Fuzzy matching is all about identifying and linking similar, but not identical strings of text. In our example above we saw that “James Smith“ and “James S“ are the same person; in exact matching there would have been no way to know that, but via fuzzy matching these two were similar enough that there was a chance that this was a match.
We’re thrilled to announce the integration of fuzzy matching afghanistan phone number list across all features in the Contact object, providing customers with the tailored solutions they require. Stay tuned for further updates as we continue to enhance this feature in future releases!
Explore More
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Salesforce AI Website: salesforceairesearch.com
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Acknowledgements
This new capability in Data Cloud is made possible through the collaboration of AI Research and Data Cloud teams.
AI Research: Zhiwei Liu, Jianguo Zhang, Shelby Heinecke, Huan Wang, Caiming Xiong, Vera Serdiukova, Silvio Savarese
Data Cloud: Stanislav Georgiev, Torrey Teats, Suresh Thalamati, Srishti Hunjan, Anthony Yeung