LongEval-Retrieval: French-English Dynamic Test Collection for Continuous Web Search Evaluation

TL;DR

LongEval-Retrieval introduces a dynamic, continuous Web search evaluation benchmark based on evolving document collections, queries, and relevance, using data from Qwant to study system persistence over time.

Contribution

It presents a novel dynamic test collection for continuous Web search evaluation, incorporating temporal evolution and bilingual data from French to English.

Findings

Constructed from Qwant search data in French and English

Provides baseline retrieval results and analysis

Simulates real-world evolving Web search environments

Abstract

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.