experimental[The ranking evaluation API is experimental and may be changed or removed completely in a future release, as well as change in non-backwards compatible ways on minor versions updates. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.]
Search quality evaluation starts with looking at the users of your search application, and the things that they are searching for.
Users have a specific _information need_, e.g. they are looking for gift in a web shop or want to book a flight for their next holiday.
They usually enters some search terms into a search box or some other web form.
All of this information, together with meta information about the user (e.g. the browser, location, earlier preferences etc...) then gets translated into a query to the underlying search system.
The challenge for search engineers is to tweak this translation process from user entries to a concrete query in such a way, that the search results contain the most relevant information with respect to the users information need.
This can only be done if the search result quality is evaluated constantly across a representative test suite of typical user queries, so that improvements in the rankings for one particular query doesn't negatively effect the ranking for other types of queries.
The ranking evaluation API provides a convenient way to use this information in a ranking evaluation request to calculate different search evaluation metrics. This gives a first estimation of your overall search quality and give you a measurement to optimize against when fine-tuning various aspect of the query generation in your application.
<2> definition of the evaluation metric to calculate
<3> a specific metric and its parameters
The request section contains several search requests typical to your application, along with the document ratings for each particular search request, e.g.
A document `rating` can be any integer value that expresses the relevance of the document on a user defined scale. For some of the metrics, just giving a binary rating (e.g. `0` for irrelevant and `1` for relevant) will be sufficient, other metrics can use a more fine grained scale.
As an alternative to having to provide a single query per test request, it is possible to specify query templates in the evaluation request and later refer to them. Queries with similar structure that only differ in their parameters don't have to be repeated all the time in the `requests` section this way. In typical search systems where user inputs usually get filled into a small set of query templates, this helps making the evaluation request more succinct.
This metric measures the number of relevant results in the top k search results. Its a form of the well known https://en.wikipedia.org/wiki/Information_retrieval#Precision[Precision] metric that only looks at the top k documents. It is the fraction of relevant documents in those first k
|`ignore_unlabeled` |controls how unlabeled documents in the search results are counted.
If set to 'true', unlabeled documents are ignored and neither count as relevant or irrelevant. Set to 'false' (the default), they are treated as irrelevant.
In contrast to the two metrics above, https://en.wikipedia.org/wiki/Discounted_cumulative_gain[discounted cumulative gain] takes both, the rank and the rating of the search results, into account.
The assumption is that highly relevant documents are more useful for the user when appearing at the top of the result list. Therefore, the DCG formula reduces the contribution that high ratings for documents on lower search ranks have on the overall DCG metric.
|`normalize` | If set to `true`, this metric will calculate the https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG[Normalized DCG].
Expected Reciprocal Rank (ERR) is an extension of the classical reciprocal rank for the graded relevance case
(Olivier Chapelle, Donald Metzler, Ya Zhang, and Pierre Grinspan. 2009. http://olivier.chapelle.cc/pub/err.pdf[Expected reciprocal rank for graded relevance].)
It is based on the assumption of a cascade model of search, in which a user scans through ranked search
results in order and stops at the first document that satisfies the information need. For this reason, it
is a good metric for question answering and navigation queries, but less so for survey oriented information
needs where the user is interested in finding many relevant documents in the top k results.
The metric models the expectation of the reciprocal of the position at which a user stops reading through
the result list. This means that relevant document in top ranking positions will contribute much to the
overall score. However, the same document will contribute much less to the score if it appears in a lower rank,
even more so if there are some relevant (but maybe less relevant) documents preceding it.
In this way, the ERR metric discounts documents which are shown after very relevant documents. This introduces
a notion of dependency in the ordering of relevant documents that e.g. Precision or DCG don't account for.
[source,js]
--------------------------------
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"expected_reciprocal_rank": {
"maximum_relevance" : 3,
"k" : 20
}
}
}
--------------------------------
// CONSOLE
// TEST[setup:twitter]
The `expected_reciprocal_rank` metric takes the following parameters:
