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 suit 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.
In order to get started with search quality evaluation, three basic things a needed:
. a collection of document you want to evaluate your query performance against, usually one or more indices
. a collection of typical search requests that users enter into your system
. a set of document ratings that judge the documents relevance with respect to a search request+
It is important to note that one set of document ratings is needed per test query, and that
the relevance judgements are based on the _information_need_ of the user that entered the query.
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.
<1> a set of typical search requests to your system
<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
search. A precision at 10 (prec@10) value of 0.6 then means six out of the 10 top hits where
relevant with respect to the users information need.
This metric works well as a first and an easy to explain evaluation metric.
Documents in the collection need to be rated either as relevant or irrelevant with respect to the current query. Prec@k does not take into account where in the top k results the relevant documents occur, so a ranking of ten results that contains one relevant
|`relevant_rating_threshold` |Sets the rating threshold from which on documents are considered to be
"relevant". Defaults to `1`.
|`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].
