1,721,097 research outputs found
Some reflections on the use of structural equation modeling for investigating the causal relationships that affect search engine results
No full textSearch engines and recommender systems pervade everyday life and continuously make decisions regarding what information should be retrieved and how it should be ranked in order to meet the user’s information needs on the user’s behalf. Unfortunately, bias affects automated decision systems and as a consequence fairness cannot be taken for granted. Understanding whether and how bias affects search results can be a necessary and useful condition to every user and designer who aims to investigate the reasons that the systems fail or succeed. In this paper, we discuss whether Structural Equation Modeling (SEM) can be a useful methodology to investigate the causal relationships between the variables describing the content representation and retrieval processes of search engines and recommender systems. Understanding how and why a retrieval system retrieves certain documents can help understand when the system provides biased results. To this end, we provide a general illustration of the issues and the potential of SEM for causal discovery in Information Retrieval
Impact of query sample selection bias on information retrieval system ranking
No full textInformation Retrieval (IR) effectiveness measures commonly assume that the experimental query sets consist of randomly drawn queries that represent the population of queries submitted to IR systems. In many practical situations, however, this assumption is violated, in a problem known as sample selection bias. It follows that the systems participating in evaluation campaigns are ranked by biased estimators of effectiveness. In this paper, we address the problem of query sample selection bias in machine learning terms and study experimentally how retrieval system rankings are affected by it. To this end, we apply a number of retrieval effectiveness measures and query probability estimation methods useful to correct sample selection bias. We report that the ranking of the most effective systems and that of the least effective systems is fairly affected by query sample selection bias, while the ranking of the average systems is much more affected. We also report that the measure of bias d..
Evaluation of a Feedback Algorithm inspired by Quantum Detection for Dynamic Search Tasks
No full textIn this paper we investigate the effectiveness of Relevance Feedback algorithms inspired by Quantum Detection in the context of the Dynamic Domain track. Documents and queries are represented as vectors; the query vector is projected into the subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. When relevant documents are present in the feedback set, the algorithm performs Explicit RF exploiting evidence gathered from relevant passages; if all the documents in the top retrieved are judged as non-relevant, Pseudo RF is performed
- …
