peterdm

3 ms·

1. 1.▲
   by peterdm 7y ago

   Are you seeing a blueprint start to emerge for a standardized way to build and deploy search indices in the context of applications that need vector-space features? (E.g. if you start with ANNOY, you get kNN but then how do you add in th
2. 2.▲
   by peterdm 7y ago

   Upvoted. This is how Postgres supports "fuzzy searching" which helps with misspellings. https://www.rdegges.com/2013/easy-fuzzy-text-searching-with-... Completion response-time will be slower than Solr, Ela
3. 3.▲
   by peterdm 7y ago

   For e-commerce search, personalization with Elasticsearch takes a similar level of effort as with Solr. Don't re-platform under the impression it will make personalization easier. It still takes data collection and experimentation bu
4. 4.▲
   by peterdm 7y ago

   Feature vectors do tend to get incorporated in relevance tuning (regardless of the engine), but from what I've heard of Vespa, features (and ML in general) are first-class citizens, whereas with Elasticsearch and Solr, text statistics
5. 5.▲
   by peterdm 7y ago

   Until about 2014-2015, many large companies wouldn't have looked twice at Elasticsearch. The companies you list using Solr have been invested in search for 10 years or longer (predating Elasticsearch), and may have high switching cost