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

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.

Towards More Accountable Search Engines: Online Evaluation of Representation Bias

Information availability affects people's behavior and perception of the world. Notably, people rely on search engines to satisfy their need for information. Search engines deliver results relevant to user requests usually without being or making themselves accountable for the information they deliver, which may harm people's lives and, in turn, society. This potential risk urges the development of evaluation mechanisms of bias in order to empower the user in judging the results of search engines. In this paper, we give a possible solution to measuring representation bias with respect to societal features for search engines and apply it to evaluating the gender representation bias for Google's Knowledge Graph Carousel for listing occupations.