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• #Apache lucene relevance models upgrade

#Apache lucene relevance models upgrade

Where BM stands for Best Matching 🙂 You can think of Okapi BM25 as an upgrade of TF-IDF, and you’ll find a detailed comparison between the two here.

Here, docLength is the (approximate) number of terms in the document. The longer the document, the more likely it is to contain that field by pure chance, so it will get a lower score (score/sqrt(docLength) will be the result). Lucene also normalizes scores by document length. The idea is to tame the curve a little bit, as docFreq (number of documents containing the term) increases: IDF value drops for more frequent terms (here with 10K docs in the index) It actually takes 1 + log( (docCount+1)/(docFreq+1)). The IDF (Inverse DF) will be very low, because cat will carry less interesting information about the documents compared to food which, presumably, has a higher IDF. For example, if you’re searching for cat AND food in an E-commerce site dedicated to cats, the DF of cat will be very high. Here, Document Frequency (DF) stands for the ratio between the number of documents containing the term and all the documents of your index. IDF stands for Inverse Document Frequency. Value of TF grows, the more the term occurs in the document Lucene actually takes the square root of the TF: if you query for cat, a document mentioning cat twice is more likely about cats, but maybe not twice as likely as a document with only one occurrence. We’re looking at one term at a time (all similarities are doing this) and, the more often the term appears in our field, the higher the score. As the name suggests, the score is calculated from multiplying TF with IDF, where: TF*IDF has been in Lucene since forever, and was the default until BM25 replaced it in version 6. Term Frequency * Inverse Document Frequency (TF*IDF)

the Divergence from Randomness framework.classic TF-IDF and the newer default BM25.In this post, we’re going to cover all the available similarity classes and their options: Whether you’re using Solr or Elasticsearch, you can choose a similarity class/framework and, depending on its choice, some options to influence how scores are calculated. The importance of things like field length or term frequency could be different.īefore we start, check out two useful Cheat Sheets to guide you through both Solr and Elasticsearch and help boost your productivity and save time when you’re working with any of these two open-source search engines.ĭownload Yours Similarity Options in Solr & Elasticsearch You’ll likely have different criteria for matching a query with the content field of a book, for example, than for the author field. The default similarity (BM25 – described below) is a very good start, but you may need to tweak it for your use-case. boost more recent documents) and re-ranking (e.g. Similarity makes the base of your relevancy score: how similar is this document (actually, this field in this document) to the query? I’m saying the base of the score because, on top of this score, you can apply per-field boosts, function scoring (e.g. By extension, Solr and Elasticsearch have the same options. Lucene has a lot of options for configuring similarity. Application Performance Monitoring Guide.