LongEval-Retrieval is a Web document retrieval benchmark that focuses on continuous retrieval evaluation. This test collection is intended to be used to study the temporal persistence of Information Retrieval systems and will be used as the test collection in the Longitudinal Evaluation of Model Performance Track (LongEval) at CLEF 2023. This benchmark simulates an evolving information system environment - such as the one a Web search engine operates in - where the document collection, the query distribution, and relevance all move continuously, while following the Cranfield paradigm for offline evaluation. To do that, we introduce the concept of a dynamic test collection that is composed of successive sub-collections each representing the state of an information system at a given time step. In LongEval-Retrieval, each sub-collection contains a set of queries, documents, and soft relevance assessments built from click models. The data comes from Qwant, a privacy-preserving Web search engine that primarily focuses on the French market. LongEval-Retrieval also provides a 'mirror' collection: it is initially constructed in the French language to benefit from the majority of Qwant's traffic, before being translated to English. This paper presents the creation process of LongEval-Retrieval and provides baseline runs and analysis.

This paper introduces a novel visualization tool that facilitates the exploratory analysis of continuous evaluation for information retrieval systems. We base our analysis on score standardization and meta-analysis techniques applied to Information Retrieval evaluation. We present three functionalities: evaluation overview, delta evaluation, and meta-analysis applied to three perspectives: evaluation rounds, queries, and systems. To illustrate the use of the tool, we provide an example using the TREC-COVID test collection.

Classical evaluation of information retrieval systems evaluates a system in a static test collection. In the case of Web search, the evaluation environment (EE) is continuously changing and the hypothesis of using a static test collection is not representative of this changing reality. Moreover, the changes in the evaluation environment, as the document set, the topics set, the relevance judgments, and the chosen metrics, have an impact on the performance measurement [1, 4]. To the best of our knowledge, there is no way to evaluate two versions of a search engine with evolving EEs.