The Relationship between IR Effectiveness Measures and User Satisfaction

This paper presents an experimental study of users assessing the quality of Google web search results. In particular we look at how users' satisfaction correlates with the effectiveness of Google as quantified by IR measures such as precision and the suite of Cumulative Gain measures (CG, DCG, NDCG). Results indicate strong correlation between users' satisfaction, CG and precision, moderate correlation with DCG, with perhaps surprisingly negligible correlation with NDCG. The reasons for the low correlation with NDCG are examined

Equity of Attention: Amortizing Individual Fairness in Rankings

Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.Comment: Accepted to SIGIR 201

Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201

Measures to Evaluate the Superiority of a Search Engine

Main objective of a search engine is to return relevant results according to user query in less time. Evaluation metrics are used to measure the superiority of a search engine in terms of quality. This is a review paper presenting a summary of different metrics used for evaluation of a search engine in terms of effectiveness, efficiency and relevancy

MedEval — A Swedish medical test collection with doctors and patients user groups

Abstract Background Test collections for information retrieval are scarce. Domain specific test collections even more so, and medical test collections in the Swedish language non-existent prior to the making of the MedEval test collection. Most research in information retrieval has been performed in the English language, thus most test collections contain English documents. However, English is morphologically poor compared to many other European languages and a number of interesting and important aspects have not been investigated. Building a medical test collection in Swedish opens new research opportunities. Methods This article describes the making of and potential uses of MedEval, a Swedish medical test collection with assessments, not only for topical relevance, but also for target reader group: Doctors or Patients. A user of the test collection may choose if she wishes to search in the Doctors or the Patients scenario where the topical relevance assessments have been adjusted with consideration to user group, or to search in a scenario which regards only topical relevance. In addition to having three user groups, MedEval, in its present form, has two indexes, one where the terms are lemmatized and one where the terms are lemmatized and the compounds split and the constituents indexed together with the whole compound. Results Differences discovered between the documents written for medical professionals and documents written for laypersons are presented. These differences may be utilized in further studies of retrieval of documents aimed at certain groups of readers. Differences between the groups of documents are, for example, that professional documents have a higher ratio of compounds, have a greater average word length and contain more multi-word expressions. An experiment is described where the user scenarios have been utilized, searching with expert terms and lay terms, separately and in combination in the different scenarios. The tendency discovered is that the medical expert gets best results using expert terms and the lay person best results using lay terms, but also quite good results using expert terms or lay and expert terms in combination. Conclusions The many features of MedEval gives a variety of research possibilities, such as comparing the effectiveness of search terms when it comes to retrieving documents aimed at the different user groups or to study the effect of compound decomposition in retrieval of documents. As Swedish, the language of MedEval, is a morphologically more complex language than English, it is possible to study additional aspects of the effect of natural language processing in information retrieval, for example utilizing different inflectional word forms in the retrieval of expert vs lay documents. MedEval is the first Swedish test collection of the medical domain. Availability The Department of Swedish at the University of Gothenburg is in the process of making the MedEval test collection available to academic researchers.© 2011 Heppin; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited