TheEffects of Decision Aid Recommendations on Users’ Cognitive Processes, Memories, and Judgments

This study extends the existing decision aid literature by examining the influence of decision aid recommendations on users’ memories, decision processes, and judgments. Existing research suggests that decision aids can be beneficial in a variety of settings. Judgments or decisions, the outputs of the decision- making process, are the focus of most of the decision aid research. This study offers a more comprehensive investigation of the impact of decision aids by examining both the outputs of the decision-making process and the inputs and processes that lead to judgment and decision-making. An experiment is conducted that examines the influence of decision aid recommendations on memory patterns, search, cue usage, and judgments. Specifically, the study focuses on how positive and negative decision aid recommendations and the timing of receipt of the decision aid recommendation differentially affect these components of the decision process. The key findings of the research are: (1) decision aid recommendations create strong affective responses that are encoded in memory and cause users to reconstruct memories of financial data to be consistent with the affective response, (2) receiving a decision aid recommendation at the start of a task creates a strong initial response that acts as an initial hypothesis wherein users’ subsequent information search patterns exhibit a confirming bias, (3) receiving a decision aid recommendation later in the task creates a strong response that initiates professional skepticism and causes users’ subsequent information search patterns to exhibit a disconfirming bias, (4) decision aid recommendations influence the choice of information cues users believe to be important, (5) decision aid recommendations exert influence on users’ judgments, with the amount of influence diminishing as additional information is received, and (6) working memory capacity is a determinant in the ability to recall financial information but does not determine the extent of influence decision aid recommendations have on users. These findings, when considered together, validate the need for a more complete examination of how decision aids impact the entire decision-making process to identify potential negative consequences in addition to proposed benefits. This research demonstrates that task structure can be manipulated to mitigate certain undesirable consequences of decision aid use

Prediction of Relevant Biomedical Documents: a Human Microbiome Case Study

Background: Retrieving relevant biomedical literature has become increasingly difficult due to the large volume and rapid growth of biomedical publication. A query to a biomedical retrieval system often retrieves hundreds of results. Since the searcher will not likely consider all of these documents, ranking the documents is important. Ranking by recency, as PubMed does, takes into account only one factor indicating potential relevance. This study explores the use of the searcher’s relevance feedback judgments to support relevance ranking based on features more general than recency. Results: It was found that the researcher’s relevance judgments could be used to accurately predict the relevance of additional documents: both using tenfold cross-validation and by training on publications from 2008–2010 and testing on documents from 2011. Conclusions: This case study has shown the promise for relevance feedback to improve biomedical document retrieval. A researcher’s judgments as to which initially retrieved documents are relevant, or not, can be leveraged to predict additional relevant documents

On-line Metasearch, Pooling, and System Evaluation

This thesis presents a unified method for simultaneous solution of three problems in Information Retrieval--- metasearch (the fusion of ranked lists returned by retrieval systems to elicit improved performance), efficient system evaluation (the accurate evaluation of retrieval systems with small numbers of relevance judgements), and pooling or ``active sample selection (the selection of documents for manual judgement in order to develop sample pools of high precision or pools suitable for assessing system quality). The thesis establishes a unified theoretical framework for addressing these three problems and naturally generalizes their solution to the on-line context by incorporating feedback in the form of relevance judgements. The algorithm--- Rankhedge for on-line retrieval, metasearch and system evaluation--- is the first to address these three problems simultaneously and also to generalize their solution to the on-line context. Optimality of the Rankhedge algorithm is developed via Bayesian and maximum entropy interpretations. Results of the algorithm prove to be significantly superior to previous methods when tested over a range of TREC (Text REtrieval Conference) data. In the absence of feedback, the technique equals or exceeds the performance of benchmark metasearch algorithms such as CombMNZ and Condorcet. The technique then dramatically improves on this performance during the on-line metasearch process. In addition, the technique generates pools of documents which include more relevant documents and produce more accurate system evaluations than previous techniques. The thesis includes an information-theoretic examination of the original Hedge algorithm as well as its adaptation to the context of ranked lists. The work also addresses the concept of information-theoretic similarity within the Rankhedge context and presents a method for decorrelating the predictor set to improve worst case performance. Finally, an information-theoretically optimal method for probabilistic ``active sampling is presented with possible application to a broad range of practical and theoretical contexts

How Relevance Feedback is Framed Affects User Experience, but not Behaviour

Retrieval systems based on machine learning require both positive and negative examples to perform inference, which is usually obtained through relevance feedback. Unfortunately, explicit negative relevance feedback is thought to have poor user experience. Instead, systems typically rely on implicit negative feedback. In this study, we confirm that, in the case of binary relevance feedback, users prefer giving positive feedback ( and implicit negative feedback) over negative feedback ( and implicit positive feedback). These two feedback mechanisms are functionally equivalent, capturing the same information from the user, but differ in how they are framed. Despite users' preference for positive feedback, there were no significant differences in behaviour. As users were not shown how feedback influenced search results, we hypothesise that previously reported results could, at least in part, be due to cognitive biases related to user perception of negative feedback.Peer reviewe

Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search