[
https://issues.apache.org/jira/browse/SOLR-17319?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alessandro Benedetti updated SOLR-17319:
----------------------------------------
Description:
Reciprocal Rank Fusion (RRF) is an algorithm that takes in input multiple
ranked lists to produce a unified result set.
Examples of use cases where RRF can be used include hybrid search and multiple
Knn vector queries executed concurrently.
RRF is based on the concept of reciprocal rank, which is the inverse of the
rank of a document in a ranked list of search results.
The combination of search results happens taking into account the position of
the items in the original rankings, and giving higher score to items that are
ranked higher in multiple lists. RRF was introduced the first time by Cormack
et al. in [1].
The syntax proposed:
JSON Request
{code:json}
{
"queries": {
"lexical1": {
"lucene": {
"query": "id:(10^=2 OR 2^=1 OR 4^=0.5)"
}
},
"lexical2": {
"lucene": {
"query": "id:(2^=2 OR 4^=1 OR 3^=0.5)"
}
}
},
"limit": 10,
"fields": "[id,score]",
"params": {
"combiner": true,
"combiner.upTo": 5,
"facet": true,
"facet.field": "id",
"facet.mincount": 1
}
}
{code}
[1] Cormack, Gordon V. et al. “Reciprocal rank fusion outperforms condorcet and
individual rank learning methods.” Proceedings of the 32nd international ACM
SIGIR conference on Research and development in information retrieval (2009)
was:
Reciprocal Rank Fusion (RRF) is an algorithm that takes in input multiple
ranked lists to produce a unified result set.
Examples of use cases where RRF can be used include hybrid search and multiple
Knn vector queries executed concurrently.
RRF is based on the concept of reciprocal rank, which is the inverse of the
rank of a document in a ranked list of search results.
The combination of search results happens taking into account the position of
the items in the original rankings, and giving higher score to items that are
ranked higher in multiple lists. RRF was introduced the first time by Cormack
et al. in [1].
The syntax proposed:
{code:json}
{
"queries": {
"lexical1": {
"lucene": {
"query": "id:(10^=2 OR 2^=1 OR 4^=0.5)"
}
},
"lexical2": {
"lucene": {
"query": "id:(2^=2 OR 4^=1 OR 3^=0.5)"
}
}
},
"limit": 10,
"fields": "[id,score]",
"params": {
"combiner": true,
"combiner.upTo": 5,
"facet": true,
"facet.field": "id",
"facet.mincount": 1
}
}
{code}
[1] Cormack, Gordon V. et al. “Reciprocal rank fusion outperforms condorcet and
individual rank learning methods.” Proceedings of the 32nd international ACM
SIGIR conference on Research and development in information retrieval (2009)
> Introduce support for Reciprocal Rank Fusion (combining queries)
> ----------------------------------------------------------------
>
> Key: SOLR-17319
> URL: https://issues.apache.org/jira/browse/SOLR-17319
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
> Components: query
> Affects Versions: 9.6.1
> Reporter: Alessandro Benedetti
> Assignee: Alessandro Benedetti
> Priority: Major
>
> Reciprocal Rank Fusion (RRF) is an algorithm that takes in input multiple
> ranked lists to produce a unified result set.
> Examples of use cases where RRF can be used include hybrid search and
> multiple Knn vector queries executed concurrently.
> RRF is based on the concept of reciprocal rank, which is the inverse of the
> rank of a document in a ranked list of search results.
> The combination of search results happens taking into account the position of
> the items in the original rankings, and giving higher score to items that
> are ranked higher in multiple lists. RRF was introduced the first time by
> Cormack et al. in [1].
> The syntax proposed:
> JSON Request
> {code:json}
> {
> "queries": {
> "lexical1": {
> "lucene": {
> "query": "id:(10^=2 OR 2^=1 OR 4^=0.5)"
> }
> },
> "lexical2": {
> "lucene": {
> "query": "id:(2^=2 OR 4^=1 OR 3^=0.5)"
> }
> }
> },
> "limit": 10,
> "fields": "[id,score]",
> "params": {
> "combiner": true,
> "combiner.upTo": 5,
> "facet": true,
> "facet.field": "id",
> "facet.mincount": 1
> }
> }
> {code}
> [1] Cormack, Gordon V. et al. “Reciprocal rank fusion outperforms condorcet
> and individual rank learning methods.” Proceedings of the 32nd international
> ACM SIGIR conference on Research and development in information retrieval
> (2009)
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