OpenSearch will run for the first time as a track at TREC in 2016.

TREC OpenSearch will be run in a the same vein as CLEF LL4IR and use the same infrastructure.

TREC OpenSearch

We define OpenSearch as follows:

OpenSearch is a new evaluation paradigm for IR. The experimentation platform is an existing search engine. Researchers have the opportunity to replace components of this search engine and evaluate these components using interactions with real, unsuspecting users of this search engine.

This definition is generic, on purpose, to allow for the replacement of, and experimentation with, any part of a search engine (including, e.g., result presentation). Our immediate focus, however, lies at the very core of a search engine: the ranking method.

Evaluation Setup

TREC OpenSearch evaluates rankings provided by participants in the context of an actual search engine, by serving precomputed runs, for a given set of queries, to the users that enter one of these queries in the real-life search engine.

The track operates as follows. A set Q of queries is taken from the logs of a real, existing, search engine. These queries are chosen such that they appear frequently enough, making it likely that they will be issued again in the near future by users of this search engine. This selection of queries is a crucial ingredient of our approach and we discussed this in more detail in earlier work. Additionally, for each query, the search engine prepares a set of candidate documents and some historical interaction data for each document. TREC OpenSearch then requires an infrastructure, an API, that allows the search engine to share queries, documents and interactions with the participants. Once the search engine uploads the data to the API, it can be downloaded by participants. This way participants are provided with very much the typical TREC-style collection, consisting of queries and documents and additionally historical interactions. Queries are strings and documents are represented as JSON documents with all the fields common in literature search (e.g., author, title, abstract, full text, etc).

Participants are expected to produce their runs, as they normally would, and upload these through the API. When an unsuspecting, real user then issues a query against the search engine, the search engine will ask the API to provide them with a run for that query. The API then selects uniformly randomly from among the runs that have already been upload by participants. This run is then returned to the search engine. The search engine interleaves the run with its production system and shows this to the user. The user may or may not interact with this ranking. When there is an interaction, the search engine sends this back to the API. And the API then makes it available to the participant. The participant can then (or at any moment for that matter) choose to update their ranking.

A subset of queries are designated as test queries, all other queries are considered train queries. For train queries, the procedure is exactly as described above. The test queries are treated largely the same as the train queries, however, there are some crucial differences. For test queries, participants never receive individual impressions of their run nor individual interactions of users with this run. Only aggregations of interleaving outcomes are reported for test queries. Moreover, during a designated test period, participants can not update the runs for test queries. This allows TREC OpenSearch to compare stable runs (and their underlying systems) to each other. Moreover, since no individual feedback is provided for test queries, participants can not optimize their systems for test queries, only for train queries. We propose this procedure to be able to learn how participants’ systems would generalize to unseen queries.

See the guidelines for further details.


The above setup has limitations. These include that only a selection of queries is considered (all queries are reasonably frequent, so no long tail) and there is no contextual information available about the current user (meaning that personalization is not possible). On the positive side, this approach avoids the privacy concerns and lowers the barrier to entry: participants prepare their runs offline and partake in an online experiment, without having to build and maintain a live service.