s-AWARE: Measure-based Supervised Merging Algorithms for Crowd Assessors in Information Retrieval

In this thesis we develop a new approach to exploit crowd assessors relevance judgements for IR evaluation. We compute evaluation measures based on each assessor's ground truth. These measures are then merged weighting each assessor on the basis of his expertise level, estimated as the closeness between the assessor measures and gold standard measures, on a training set. The results highlight the greater performance of s-AWARE approach with respect to the majority of tested approaches

Cheap IR Evaluation: Fewer Topics, No Relevance Judgements, and Crowdsourced Assessments

To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of information needs (called topics), and a set of relevant documents to each topic. Test collections are modelled in a competition scenario: for example, in the well known TREC initiative, participants run their own retrieval systems over a set of topics and they provide a ranked list of retrieved documents; some of the retrieved documents (usually the first ranked) constitute the so called pool, and their relevance is evaluated by human assessors; the document list is then used to compute effectiveness metrics and rank the participant systems. Private Web Search companies also run their in-house evaluation exercises; although the details are mostly unknown, and the aims are somehow different, the overall approach shares several issues with the test collection approach. The aim of this work is to: (i) develop and improve some state-of-the-art work on the evaluation of IR effectiveness while saving resources, and (ii) propose a novel, more principled and engineered, overall approach to test collection based effectiveness evaluation. [...