Hello Floyd, There is a ton of research literature out there comparing BM25 to vector space. But you have to be careful interpreting it.
BM25 originally beat the SMART vector space model in the early TRECs because it did better tf and length normalization. Pivoted Document Length normalization was invented to get the vector space model to catch up to BM25. (Just Google for Singhal length normalization. Amith Singhal, now chief of Google Search did his doctoral thesis on this and it is available. Similarly Stephan Robertson, now at Microsoft Research published a ton of studies of BM25) The default Solr/Lucene similarity class doesn't provide the length or tf normalization tuning params that BM25 does. There is the sweetspot simliarity, but that doesn't quite work the same way that the BM25 normalizations do. Document length normalization needs and parameter tuning all depends on your data. So if you are reading a comparison, you need to determine: 1) When comparing recall/precision etc. between vector space and Bm25, did the experimenter tune both the vector space and the BM25 parameters 2) Are the documents (and queries) they are using in the test, similar in length characteristics to your documents and queries. We are planning to do some testing in the next few months for our use case, which is 10 million books where we index the entire book. These are extremely long documents compared to most IR research. I'd love to hear about actual (non-research) production implementations that have tested the new ranking models available in Solr. Tom On Wed, Nov 14, 2012 at 9:16 PM, Floyd Wu <floyd...@gmail.com> wrote: > Hi there, > Does anybody can kindly tell me how to setup solr to use BM25? > By the way, are there any experiment or research shows BM25 and classical > VSM model comparison in recall/precision rate? > > Thanks in advanced. >
