I was having the discussion with a colleague of mine recently, about E-commerce search. Of course there are tons of things you can do to improve relevancy: Custom similarity - edismax tuning - basic user events processing - machine learning integrations - semantic search ect ect
more you do, better the results will potentially be, basically it is an ocean to explore. To avoid going off topic and being pertinent to your initial request, let's take a look to the custom similarity problem. In e-commerce, and generally in proper nouns searches TF is not relevant. IDF can help, but we need to focus on what IDF is used for in general, in lucene search : Mostly IDF is a measure of "how much this term is important in the user query". Basically Lucene ( and in general TF/IDF based Information Retrieval systems ) assume that more a term is rare in the corpus, more likely it is that it is important for the search query. That is not always true in e-commerce : "iphone cover" means the user is looking for a cover, which is good for his/her phone. iphone is rare. Cover is not. IDF will recognize "Iphone" to be the most pertinent term to the user intent. There's a lot to talk in here, let's stop :) Anyway as a conclusion, go step by step, custom similarity + edismax optimised with proper phrase and shingle boosts should be a good start. Tie-breaking for e-commerce is likely to be ok, set to the default. But to discover that I would recommend to set up a relevancy measuring framework with golden queries and users feedback. cheers ----- --------------- Alessandro Benedetti Search Consultant, R&D Software Engineer, Director Sease Ltd. - www.sease.io -- Sent from: http://lucene.472066.n3.nabble.com/Solr-User-f472068.html
