I have content with various numeric values, and a higher value indicates (theoretically) more valuable content, which I want to rank higher.
For instance:
Average rating (0 - 5)
Number of comments (0 - whatever)
Number of inbound link references from other pages (0 - whatever)
Some arbitrary number I apply to indicate how important I feel the content is (1 - whatever)
These can be indexed by Lucene as a numeric value, but how can I tell Lucene to use this value in its ranking algorithm?
you can set this value using "Field.SetBoost" while indexing.
Depending how exactly you want to proceed, you can set boost while indexing as suggested by #L.B, or if you want to make it dynamic, i.e. at search time rather than indexing time, you can use ValueSourceQuery and CustomScoreQuery.
You can see example in the question I asked some time ago:
Lucene custom scoring for numeric fields (the example was tested with Lucene 3.0).
Related
Using SOLR version 4.3, it appears that SOLR is valuing the percentage of matching terms more than the number of matching terms.
For example, we do a search for Dog and a document with just the word dog and a three other words returns. We have another article with hundreds of words, that has the word dog in it 27 times.
I would expect the second article to return first. However, the one with one word out of three returns first. I was hoping to find out what in SOLR controls this so I can make the appropriate modifications. I have looked the SOLR documentation and have seen COORD mentioned, but it seems to indicate that the article with 27 references should return first. Any help would be appreciated.
For 4.x Solr still used regular TF/IDF as its scoring formula, and you can see the Lucene implementation detailed in the documentation for TFIDFSimilarity.
For your question, the two factors that affect the score is:
The length of the field, as given in norm():
norm(t,d) encapsulates a few (indexing time) boost and length factors:
Field boost - set by calling field.setBoost() before adding the field to a document.
lengthNorm - computed when the document is added to the index in accordance with the number of tokens of this field in the document, so that shorter fields contribute more to the score. LengthNorm is computed by the Similarity class in effect at indexing.
.. while the number of terms matching (not their frequency), is given by coord():
coord(q,d) is a score factor based on how many of the query terms are found in the specified document. Typically, a document that contains more of the query's terms will receive a higher score than another document with fewer query terms. This is a search time factor computed in coord(q,d) by the Similarity in effect at search time.
There are a few settings in your schema that can affect how Solr scores the documents in your example:
omitNorms
If true, omits the norms associated with this field (this disables length normalization for the field, and saves some memory)
.. this will remove the norm() part of the score.
omitTermFreqAndPositions
If true, omits term frequency, positions, and payloads from postings for this field.
.. and this will remove the boost from multiple occurrences of the same term. Be aware that this will remove positions as well, making phrase queries impossible.
But you should also consider upgrading Solr, as the BM25 similarity that's the default from 6.x usually performs better. I can't remember if a version is available for 4.3.
I am working on an image retrieval task. I have a dataset of wikipedia images with their textual description in xml files (1 xml file per image). I have indexed those xmls in Solr. Now while retrieving those, I want to maintain some threshold for Score values, so that docs with less score will not come in the result (because they are not of much importance). For example I want to retrieve all documents having similarity score greater than or equal to 2.0. I have already tried range queries like score:[2.0 TO *] but can't get it working. Does anyone have any idea how can I do that?
What's the motivation for wanting to do this? The reason I ask, is
score is a relative thing determined by Lucene based on your index
statistics. It is only meaningful for comparing the results of a
specific query with a specific instance of the index. In other words,
it isn't useful to filter on b/c there is no way of knowing what a
good cutoff value would be.
http://lucene.472066.n3.nabble.com/score-filter-td493438.html
Also, take a look here - http://wiki.apache.org/lucene-java/ScoresAsPercentages
So, in general it's bad to cut off by some value, because you'll never know which threshold value is best. In good query it could be score=2, in bad query score=0.5, etc.
These two links should explain you why you DONT want to do it.
P.S. If you still want to do it take a look here - https://stackoverflow.com/a/15765203/2663985
P.P.S. I recommend you to fix your search queries, so they will search better with high precision (http://en.wikipedia.org/wiki/Precision_and_recall)
I have 2 documents, and am searching for the keyword "Twitter". Suppose both documents are blog posts with a "tags" field.
Document A has ONLY 1 term in the "tags" field, and it's "Twitter".
Document B has 100 terms in the "tags" field, but 3 of them is "Twitter".
Elastic Search gives the higher score to Document A even though Document B has a higher frequency. But the score is "diluted" because it has more terms. How do I give Document B a higher score, since it has a higher frequency of the search term?
I know ElasticSearch/Lucene performs some normalization based on the number of terms in the document. How can I disable this normalization, so that Document B gets a higher score above?
As the other answer says it would be interesting to see whether you have the same result on a single shard. I think you would and that depends on the norms for the tags field, which is taken into account when computing the score using the tf/idf similarity (default).
In fact, lucene does take into account the term frequency, in other words the number of times the term appears within the field (1 or 3 in your case), and the inverted document frequency, in other words how the term is frequent in the index, in order to compare it with other terms in the query (in your case it doesn't make any difference if you are searching for a single term).
But there's another factor called norms, that rewards shorter fields and take into account eventual index time boosting, which can be per field (in the mapping) or even per document. You can verify that norms are the reason of your result enabling the explain option in your search request and looking at the explain output.
I guess the fact that the first document contains only that tag makes it more important that the other ones that contains that tag multiple times but a lot of ther tags as well. If you don't like this behaviour you can just disable norms in your mapping for the tags field. It should be enabled by default if the field is "index":"analyzed" (default). You can either switch to "index":"not_analyzed" if you don't want your tags field to be analyzed (it usually makes sense but depends on your data and domain) or add the "omit_norms": true option in the mapping for your tags field.
Are the documents found on different shards? From Elastic search documentation:
"When a query is executed on a specific shard, it does not take into account term frequencies and other search engine information from the other shards. If we want to support accurate ranking, we would need to first execute the query against all shards and gather the relevant term frequencies, and then, based on it, execute the query."
The solution is to specify the search type. Use dfs_query_and_fetch search type to execute an initial scatter phase which goes and computes the distributed term frequencies for more accurate scoring.
You can read more here.
I know there is a NumericRangeQuery in Lucene but is it possible to have lucene simply return the maximum value stored in in a NumericField. I can use a RangeQuery over the entire known range and then sort but this is extremely cumbersome and it may return a huge amount of results if there are a lot of records
The second parameter of IndexSearcher.search(Query query, int n, Sort sort) allows to specify the top n hits (in your case 1), which, if you sort correctly, only returns the desired result. There are other overloaded methods that allow achieving the same.
Can't argue about the cumbersomeness though :)
You could Term Enum through your index. Unfortunately I don't think they're sorted in a way which makes finding the maximum instantaneous, but at least you won't have to do an actual search to find it. You will need to use NumericUtils to convert from Lucene's internal structure to a normal number.
This thread contains an example.
I am storing various articles in my lucene index.
When user searches for articles which contain a specific term or phrase,I need to show all th articles (could be anywhere between 1000 to 10000 articles) but with newest articles "bubbled up" in the search results.
I believe you can bubble up a search result in Lucene using "Date field Boosting".
Can someone please give me the details of how to go about this?
Thanks in advance!
I would implement the SortComparatorSource interface. You should write a new ScoreDocComparator, whose compare() function compares two dates. Then you will need to sort your searches using the new sorter. This advice is taken from chapter 6 of Lucene in Action.
You can use the setBoost method to set the "boost" for a particular document in the index at index time. Since the default boost value is 1.0, setting a value less than 1.0 will make the document "less relevant" in search results. By tying the boost value of a document to its age (lower boost the older the document gets), you can make newer content seem more relevant in search results.
Note in the documentation for setBoost that the boost value set at indexing time is not available for retrieved documents (boost works, you just can't read the value back at retrieval time to see if you applied the correct value at index time).