Rated Ranking Evaluator: Help the poor (Search Engineer)
A Software Engineer is always required to give his customers a concrete evidence about deliverables quality. A Search Engineer deals with a specialisation of such generic Software Quality, which is called Search Quality.
What is Search Quality? And why is it so important in a search infrastructure? After all, the “Software Quality” should be omni-comprensive, it should always include everything (and actually it is), but when we are dealing with search systems, the quality is a very abstract term, which is very hard to define in advance.
The functional correctness of a search infrastructure (assuming the correctness is the only factor which influences the system quality – and it isn’t) is naturally associated with human judgments, with opinions, and unfortunately we know opinions can be different among people.
The business stakeholders, which will get a value from a search system, can belong to different categories, can have different expectations, and they can have in mind a different idea about the expected system correctness.
In this scenario a Search Engineer is facing many challenges in terms of choices, and at the end, he has to provide concrete evidences about the functional coverage of those choices.
This is the context where we developed the Rated Ranking Evaluator (hereafter RRE).
What It Is?
The Rated Ranking Evaluator (RRE) is a search quality evaluation tool which evaluates the quality of results coming from a search infrastructure.
It helps a Search Engineer in his daily job. Are you a Search Engineer? Are you tuning/implementing/changing/configuring a search infrastructure? Do you want to have something that gives you an evidence about the improvements between changes? RRE could give you a hand on that.
RRE formalises how well a search system satisfies the user information needs, at “technical” level, combining a rich tree-like domain model with several evaluation measures, but also at “functional” level, providing human-readable outputs that could target the business stakeholders.
It encourages an incremental/iterative/immutable approach during the deveoopment and the evolution of a search system: assuming we’re starting our system from version x.y: when it’s time to apply some relevant change to its configuration, instead of applying changes to x.y, is better to clone it and apply those changes to the new fresh version.
In this way, RRE will execute the evaluation process on all available versions, it will provide the delta/trend between subsequent versions, so you can immediately get a fine-grained picture about where the system is going, in terms of relevance.
This post is only a brief summary about RRE. You can find more detailed information in the project Wiki.
In A Few Words, What Can I Get From RRE?
You can configure RRE as a compounding part of your project build cycle. That means, every time a build is triggered, an evaluation process will be executed.
RRE is not tied to a given search platform: it provides a mini-framework for plugging-in different search platforms. At the moment we have two available bindings: Apache Solr and Elasticsearch (see here for supported versions).
The output evaluation data will be available:
- as a JSON file: for further elaborations
- as a spreadsheet: for delivering the evaluation results to someone else (e.g. a business stakeholder)
- in a Web Console where metrics and their values get refreshed in real time (after each build)
How It Works
RRE provides a rich, composite, tree-like, domain model, where the evaluation concept can be seen at different levels.
The Evaluation at the top level is just a container of the nested entities. Note that all entities relationships are 1 to many. In this context, a Corpus is defined as a test dataset. RRE will use it for executing the evaluation process; in a single evaluation process you can have multiple datasets.
A Topic is an information need: it defines a functional requirement on the end-user perspective. Within a topic we can have several queries, which express the same need but more close to a technical layer. RRE provides a further abstraction in the middle: query groups. A Query Group is a group of queries which are supposed to produce the same results (and therefore are associated with the same judgments set).
Queries, which are the technical leaves of RRE domain model, are furtherly decomposed in several perspectives, one for each available version of our system. A query itself is of course a single entity, but during an evaluation session, its concrete execution happens several times, one for each available version. That because RRE needs to measure the search results (i.e. the query executions) against all versions.
For each version we will finally have one or more metrics, depending on the configuration. Last but not least, even if metrics are computed at query/version level, RRE will aggregate those values at upper levels (see the dashed vertical lines in the diagram) so each entity/level in the domain model will offer an aggregate perspective of all available metrics (i.e I could be interested in the NDCG for a given query, or I could just stop my analysis at a topic level).
Input
In order to execute an evaluation process, RRE needs the following things:
- One or more corpus / test collection: these are the representative datasets of a specific domain, that will be used for populating and querying a target search platform
- One or more configuration sets: although there’s nothing against having one single configuration, a minimum of two versions are required in order to provide a comparison between evaluation measures.
- One or more ratings sets: this is where judgments are defined, in terms of relevant documents for each query group.
Output
The RRE concrete output depends on the runtime container where it is running. The RRE core itself is just a library, so when used programmatically within a project, it outputs a set of objects corresponding to the domain model described above.
When it is used as a Maven plugin, it primarily outputs the same structure in JSON format. This data is then used for producing further outputs, like a spreadsheet. The same payload can be sent to another module called RRE Server, which offers an AngularJS based web console that gets automatically refreshed.
The RRE console is very useful when we are doing internal iterations / tries around some issue, which usually requires very short edit-and-immediately-check cycles. Imagine if you can have a couple of monitors on your desk: in the first there’s your favourite IDE, where you change things, run builds. In the second there’s the RRE Console (see below). After each build, just have a look on the console in order to get an immediate feedback of your changes.
Future Works
- integration with some tool for building the relevance judgments. That could be some UI or a more sophisticated user interaction collector (which will automatically generates the ratings sets on top of computed online metrics like click through rate, sales rate)
- Jenkins plugin: for a better integration of RRE into the popular CI tool
- Gradle plugin
- Apache Solr Rank Eval API: using the RRE core we could implement a Rank Eval endpoint in Solr, similar to the Rank Eval API provided in Elasticsearch
- ??? Other? Any suggestion is warmly welcome!
Links
- The project repository
- The project Wiki
- The slides of our talk at Apache Solr/Lucene meetup, in London
- The excellent summary of the London meetup by Flax