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.
Related
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.
I was wondering if I can count on the results order of Solr queries if the queries were not sorted.
For example:
Lets assume there are 100 documents and I want to provide paging by running 10 queries of 10 docs each, where I increment the start position each time.
If I will run a *:* 10 times while increment the start position by 10 each time can I assume I'll get all 100 docs or since there is no sorting each time I'll get a different random 10 documents.
I know that in SQL databases it worn't work, I was wondering if Solr is different.
Solr is different. In case that you do not specify a sort order, it will sort by score. In case the score is equal, it will sort by the moment a document got indexed.
In case that you query for *:* (MatchAllDocsQuery) all documents do have the same score and will be returned in the order they got indexed, as described in the SO question How are results ordered in solr in a "match all docs" query
When would the order change? In case one of the documents gets updated. Then it will fall behind its' older brothers.
But Solr has a RandomSortField for this matter:
Utility Field used for random sorting. It should not be passed a value.
This random sorting implementation uses the dynamic field name to set the random 'seed'. To get random sorting order, you need to use a random dynamic field name.
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 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.
In boolean retrieval model query consist of terms which are combined together using different operators. Conjunction is most obvious choice at first glance, but when query length growth bad things happened. Recall dropped significantly when using conjunction and precision dropped when using disjunction (for example, stanford OR university).
As for now we use conjunction is our search system (and boolean retrieval model). And we have a problem if user enter some very rare word or long sequence of word. For example, if user enters toyota corolla 4wd automatic 1995, we probably doesn't have one. But if we delete at least one word from a query, we have such documents. As far as I understand in Vector Space Model this problem solved automatically. We does not filter documents on the fact of term presence, we rank documents using presence of terms.
So I'm interested in more advanced ways of combining terms in boolean retrieval model and methods of rare term elimination in boolean retrieval model.
It seems like the sky's the limit in terms of defining a ranking function here. You could define a vector where the wi are: 0 if the ith search term doesn't appear in the file, 1 if it does; the number of times search term i appears in the file; etc. Then, rank pages based on e.g. Manhattan distance, Euclidean distance, etc. and sort in descending order, possibly culling results with distance below a specified match tolerance.
If you want to handle more complex queries, you can put the query into CNF - e.g. (term1 or term2 or ... termn) AND (item1 or item2 or ... itemk) AND ... and then redefine the weights wi accordingly. You could list with each result the terms that failed to match in the file... so that the users would at least know how good a match it is.
I guess what I'm really trying to say is that to really get an answer that works for you, you have to define exactly what you are willing to accept as a valid search result. Under the strict interpretation, a query that is looking for A1 and A2 and ... Am should fail if any of the terms is missing...