Search
Information Retrieval conference:
https://www.algolia.com/doc/guides/building-search-ui/what-is-instantsearch/js/
IR topics:
https://sigir.org/sigir2020/ - Refer this link for the list of topics.
https://blogs.dropbox.com/tech/2018/09/architecture-of-nautilus-the-new-dropbox-search-engine/
https://doc.lucidworks.com/fusion-server/5.0/solr-reference-guide/7.5.0/morelikethis.html
https://doc.lucidworks.com/fusion-ai/latest/user-guide/signals/index.html
https://lucidworks.com/post/ai-search-conference-experts/
https://lucidworks.com/ebooks/ -- IMP
https://www.slideshare.net/treygrainger/building-a-real-time-solrpowered-recommendation-engine
https://www.slideshare.net/treygrainger/building-search-and-recommendation-engines
https://dzone.com/articles/solr-and-elasticsearch
https://opensourceconnections.com/blog/2016/09/09/better-recsys-elasticsearch/
https://uxplanet.org/best-practices-for-search-results-1bbed9d7a311
https://www.crazyegg.com/blog/everything-about-semantic-search/
https://findwise.com/blog/solr-or-elasticsearch/
https://www.mabl.com/blog/choosing-between-elasticsearch-and-solr
Open Semantic Search¶
https://www.opensemanticsearch.org/
Conference¶
https://lucidworks.com/post/ai-search-conference-experts/
Semantic Search : Fries vs French Fries
Search Use Cases :
Semantic Search
Query expansion
Semantic Recommendations
Personalized Recommendations
Users + Restaurants + Menus = Grubhub Food Universe
- Query2Vec - Understanding users
- Rest2vec - Understanding restaurants (Powers discovery and personalization)
- FastMenu - Understanding menus
Query2Vec
- Query Understanding - Language Normalization and Intent Classification
- Query Building - Filtering, Query Expansion
- Candidate Selection - Phrase/Term Matching/ Semantic Matching
- Ranking - Revenue/Relevance/Personlization
- Enrichment - Pruning/Hydration/Pagination
https://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79
https://news.ycombinator.com/item?id=21448824
https://www.slideshare.net/AlexEgg1/discover-yourlatentfoodgraphwiththis1weirdtrick-pydata-nyc-2019
For example, with this query expansion technique, we can service search queries in small markets (not a lot of restaurants) more completely by showing something instead of nothing. Or in big markets, we can service obscure, tail queries by fining something similar. This helps increase the overall recall of the search system, making it better!
tdebatty/java-string-similarity
Jarowinkler : https://stackoverflow.com/questions/45233850/potential-bug-in-apaches-jaro-winkler-implementation