Is this correct behavior or do I need to do some extra setting?
I created custom filter where remove special characters and add synonyms
the search in Solr is a two step process, first the documents are matched, and second they are scored for ordering the results. The scoring takes in account how near are one term to the other, so if the order of the words in the query change, the scoring is affected.
if you omit the storing of word positions with omitPositions="true" in the field definition, and then the search should not be affected by the word order. In the Solr fields documentation you have many more options and how they affect the search described.
Related
I'm using Apache Solr for conducting search queries on some of my computer's internal documents (stored in a database). I'm getting really bizarre results for search queries ordered by descending relevancy. For example, I have 5 words in my search query. The most relevant of 4 results, is a document containing only 2 of those words multiple times. The only document containing all the words is dead last. If I change the words around in just the right way, then I see a better ranking order with the right article as the most relevant. How do I go about fixing this? In my view, the document containing all 5 of the words, should rank higher than a document that has only two of those words (stated more frequently).
What Solr did is a correct algorithm called TF-IDF.
So, in your case, order could be explained by this formula.
One of the possible solutions is to ignore TF-IDF score and count one hit in the document as one, than simply document with 5 matches will get score 5, 4 matches will get 4, etc. Constant Score query could do the trick:
Constant score queries are created with ^=, which
sets the entire clause to the specified score for any documents
matching that clause. This is desirable when you only care about
matches for a particular clause and don't want other relevancy factors
such as term frequency (the number of times the term appears in the
field) or inverse document frequency (a measure across the whole index
for how rare a term is in a field).
Possible example of the query:
text:Julian^=1 text:Cribb^=1 text:EPA^=1 text:peak^=1 text:oil^=1
Another solution which will require some scripting will be something like this, at first you need a query where you will ask everything contains exactly 5 elements, e.g. +Julian +Cribb +EPA +peak +oil, then you will do the same for combination of 4 elements out of 5, if I'm not mistaken it will require additional 5 queries and back forth, until you check everything till 1 mandatory clause. Then you will have full results, and you only need to normalise results or just concatenate them, if you decided that 5-matched docs always better than 4-matched docs. Cons of this solution - a lot of queries, need to run them programmatically, some script would help, normalisation isn't obvious. Pros - you will keep both TF-IDF and the idea of matched terms.
For a specific facet field of our Solr documents, it would make way more sense to be able to sort facets by their relative "interesting-ness" i.e. their tf-idf score, rather than by popularity. This would make it easy to automatically get rid of unwanted common English words, as both their TF and DF would be high.
When a query is made, TF should be calculated, using all the documents that participate in teh results list.
I assume that the only problem with this approach would be when no query is made, resp., when one searches for ":". Then, no term will prevail over the others in terms of interestingness. Please, correct me if I am wrong here.
Anyway,is this possible? What other relative measurements of "interesting-ness" would you suggest?
facet.sort
This param determines the ordering of the facet field constraints.
count - sort the constraints by count (highest count first) index - to
return the constraints sorted in their index order (lexicographic by
indexed term). For terms in the ascii range, this will be
alphabetically sorted. The default is count if facet.limit is greater
than 0, index otherwise.
Prior to Solr1.4, one needed to use true instead of count and false
instead of index.
This parameter can be specified on a per field basis.
It looks like you couldn't do it out of the box without some serious changes on client side or in Solr.
This is a very interesting idea and I have been searching around for some time to find a solution. Anything new in this area?
I assume that for facets with a limited number of possible values, an interestingness-score can be computed on the client side: For a given result set based on a filter, we can exclude this filter for the facet using the local params-syntax (!tag & !ex) Local Params - On the client side, we can than compute relative compared to the complete index (or another subpart of a filter). This would probably not work for result sets build by a query-parameter.
However, for an indexed text-field with many potential values, such as a fulltext-field, one would have to retrieve df-counts for all terms. I imagine this could be done efficiently using the terms component and probably should be cached on the client-side / in memory to increase efficiency. This appears to be a cumbersome method, however, and doesn't give the flexibility to exclude only certain filters.
For these cases, it would probably be better to implement this within solr as a new option for facet.sort, because the information needed is easily available at the time facet counts are computed.
There has been a discussion about this way back in 2009.
Currently, with the larger flexibility of facet.json, e.g. sorting on stats-facets (e.g. avg(price)) of another field, I guess this could be implemented as an additional sort-option. At least for facets of type term, the result-count (df for current result-set) only needs to be divided by the df of that term for the index (docfreq). If the current result-set is the complete index, facets should be sorted by count.
I will probably implement a workaround in the client for fields with a fixed and rather small vocabulary, e.g. based on a second, cashed query on the complete index. However, for term-fields and similar this might not scale.
We are consolidating all our collected content on a record in a single content field, which is the main source for SOLR. The problem is that for some records the content field has only 100K characters, for others 10M or more.
As a result, a search on any term will push 10M character records to the top of the result list.
We would like to limit/counterbalance that by introducing something like "relative term frequency" eg the number of occurrences divided by total number of words in the content field.
Since we don't know what terms people will search on, (I think) we cannot calculate this at indexing time.
Any suggestions/ideas on how to do this?
You can start with the Custom Similarity class.
This would allow you to modify the above parameters and scoring factors.
You need to check the tf (term frequency) method and customized it.
The Custom Similarity class can be refereed from the Schema.xml file.
Check the lucene DefaultSimilarity class for reference which is the actual implementation.
Also check Changing Similarity
I am using solr search with faceting in my application. My use case is in such a way that the index files in the datadir keeps on changing.
The problem is, when I facet based on a particular field. I get the value from the indices that where previously in the data dir (and are not present currently). However they are returned with a value of 0. I don't understand where the values from the previous indices are persisted and are returned during a totally newer search?
Though I can simply skip the facets with count 0, I understand that this can seriously eat over my scalability. Any pointers to not include the facets from previous searchers?
[Edit 1] : The current workaround I am using is add a facet.mincount=1 in my URL. But still, I guess this can eat over my performance.
I couldnt find a comment option & I dont have enough reputation to vote-up!
I have the same exact problem.
We are using atomic updates with solr 4.2.
I found some explanation here: http://collab.sakaiproject.org/pipermail/oae-dev/2011-November/000693.html
Excerpt:
To efficiently handle facets for multi-valued fields (like tags), Solr
builds an "uninverted index" (which you think would just be called an
"index", but I suppose that's even more confusing), which maps
internal document IDs to the list of terms they contain. Calculating
facets from this data structure just requires walking over every
document in the result set, looking up the terms it contains in the
uninverted index, and adding them to the tally for all documents.
However, there's a sneaky optimisation here that causes the zero
counts we're seeing. For terms that appear in more than 5% of
documents, Solr doesn't include them in the uninverted index (leaving
them out helps to keep the size in memory down, I guess), and instead
gets the count for these terms using a regular query against the
Lucene index. Since the set of "common" terms isn't specific to your
result set, and since any given result set won't necessarily contain
all of these terms, you can get back counts of zero.
It may not be from old index values but just terms that exist in more than 5% of documents?
I think facet.mincount=n is not a workaround, you should use it to get only the non-negative facet count.
solrQuery.setQuery("*:*");
solrQuery.addFacetField("foobar");
solrQuery.setFacetMinCount(1);
I am confuse her but i want to clear my doubt. I think it is stupid question but i want to know.
Use a TokenFilter that outputs two tokens (one original and one lowercased) for each input token. For queries, the client would need to expand any search terms containing upper case characters to two terms, one lowercased and one original. The original search term may be given a boost, although it may not be necessary given that a match on both terms will produce a higher score.
text:NeXT ==> (text:NeXT^10 OR text:next)
what this ^ mean here .
http://wiki.apache.org/solr/SolrRelevancyCookbook#Relevancy_and_Case_Matching
This is giving a boost (making it more important) to the value NeXT versus next in this query. From the wiki page you linked to "The original search term may be given a boost, although it may not be necessary given that a match on both terms will produce a higher score."
For more on Boosting please see the Boosting Ranking Terms section in your the Solr Relevancy Cookbook. This Slide Deck about Boosting from the Lucene Revolution Conference earlier this year, also contains good information on how boosting works and how to apply it to various scenarios.
Edit1:
For more information on the boost values (the number after the ^), please refer to the following:
Lucene Score Boosting
Lucene Similarity Implementation
Edit2:
The value of the boost influences the score/relevancy of an item returned from the search results.
(term:NeXT^10 term:next) - Any documents matching term:NeXT will be scored higher/more relevant in this query because they have a boost value of 10 applied.
(term:NeXT^10 term:Next^5 term:next) - Any documents matching term:NeXT will be scored the highest (because of highest boost value), any documents matching term:Next will be scored lower than term:NeXT, but higher than term:next.