Huynh, Van-Nam

Palomares Carrascosa, Ivan

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                          Palomares Carrascosa, I., & Huynh, V-N. (2017). Uncertainty Handling inRecommender and Decision Support Systems: Preface to IFSA-SCIS 2017Special Session 02. Paper presented at Joint 17th World Congress ofInternational Fuzzy Systems Association  and 9th International Conferenceon Soft Computing and Intelligent Systems, Otsu, Japan.Peer reviewed versionLink to publication record in Explore Bristol ResearchPDF-documentUniversity of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-terms.htmlSS02: Uncertainty Handling in Recommender andDecision Support SystemsIva´n Palomares CarrascosaMerchant Venturers School of EngineeringUniversity of BristolBristol, United Kingdomi.palomares@bristol.ac.ukVan-Nam HuynhSchool of Knowledge ScienceJapan Advanced Institute of Science and TechnologyNomi, Japanhuynh@jaist.ac.jpThe explosion of available data in decision support domains and their rapidly growing scal-ability, are naturally accentuating the need for capably dealing with uncertainty and imperfectinformation in order to face complex decision-making situations. Examples of such domainsinclude medical diagnosis systems, personalization and item recommendation in the Internet,planning by autonomous systems and sustainable energy policy management, to name a few. In-tegrating and handling uncertain information originated by imperfect, imprecise - and sometimesconflicting - data sources, becomes therefore a cornerstone task to procure reliable, informed de-cisions. For instance, recent research trends in recommender systems design take account ofa wider range of contextual knowledge about users, products and services, preferences, socialinteraction, etc. The necessity of approaches to effectively deal with disparate sources of infor-mation pervaded by uncertainty, has has long been an active subject of research, with significantprogress made in the last decades. Most advances achieved within the aforementioned decision-making contexts, have undeniably laid their foundations on several well consolidated UncertaintyTheories in the discipline, such as: the classical (but still widely adopted) probability theory,Zadeh’s fuzzy set theory, its extensions into possibilistic theory and the methodology of “Com-puting with Words”, and Dempster-Shafer’s Theory of Evidence.This Special Session brings together a number of recent research trends in Uncertain Infor-mation Handling within the contexts of Decision Support and Recommender Systems, and theirPractical Applications. A total of seven contributions have been accepted for publication inthis session, with a significant focus on reasoning, data fusion and decision-making processes inscenarios whose information dealt with exhibit different forms of uncertainty. Likewise, ongoingchallenges and directions for future work in this area of research are set forth by the authors.In the first contribution, Uncertain Information Fusion and Knowledge Integration: How toTake Reliability into Account, Nguyen et al. investigate the problem of uncertain informationfusion and knowledge integration from multiple sources with varying reliability. The authorshighlight the importance of considering reliability in addition to the other forms of uncertaintythat may appear, in situations when sensors record wrong measurements or human expertinformation could possibly be incorrect. A twofold approach that extends existing MaximumLikelihood-based methods is postulated, taking account of reliability in the fusion and integrationprocess of fuzzy and probabilistic uncertain information.Martin and Azvine in Graded Associations in Situation Awareness, expound the importanceof bridging the gap between crisp representations of information used by computers, and theirvague human counterparts. Accordingly, they present a mathematical framework that, by com-bining formal concept analysis and graded tolerance relations (based on fuzzy set theory), allowsto define graded fuzzy representations of associations involving both crisp and imprecise data,namely via formal concept lattices. The potential applicability of the proposed framework isdemonstrated in the context of frequent item-set discovery and, more generally, in associationrule discovery problems upon transactional data.The third contribution, An Interval Type-2 Fuzzy Model for Review Topic based Recommen-dation, considers the problem of modeling the uncertainty underlying diversity in the context ofreview topic-based recommender systems. In their paper, Ma et al. motivate the usefulness ofinterval type-2 fuzzy sets to handle and meaningfully describe diversity in topic distributions.The authors develop a novel dual topic sampling method, followed by an illustrative exampleand performance comparison against other recommender methods, demonstrating the practicalbenefits of conducting dual sampling to handle uncertainty in topic-based recommender domains.In Probabilistic Assumption-based Argumentation with DST Evidence, Hung investigates therelationship between Probabilistic Assumption-based Argumentation (PABA) and Dempster-Shafer Theory of Evidence. The paper outlines the semantic relationship between Probabilis-tic Argumentation and the belief-plausibility functions that typically characterize a body ofevidence. Subsequently, Hung defines an extended PABA framework that can naturally man-age evidential information in the fusion, reasoning and decision-making processes, contrary totraditional PABA frameworks which formerly dealt with logical knowledge exclusively in theaforementioned processes.Oki and Inuiguchi study the binary classification problem in A Mixture Model Approach toUtilizing Published Decision Rules. Concretely, they propose a mixture model that combinespublished and individually owned rule bases stemming from different datasets. Their approachconstructs and conflates score distributions over classes, with each distribution describing theextent to which new data instances may belong to the existing class labels. A comparative studyshows that the accuracy in Oki and Inuiguchi’s mixture approach outperforms similar baselinemethods under the presence of biased data.In An Axiomatic Approach to the Estimation of Interval-valued Preferences in Multi-CriteriaDecision Modeling, Franco et al. explore the interval extensions of popular models for multi-criteria decision support to deal with imprecision. Based on an axiomatic analysis of preferencemodeling, some weaknesses are identified, including the difficulty to reason over the uncertaintyof interval-valued decision outcomes. In their study, the authors introduce a number of axiomsfor interval-valued preference estimation, concluding that the Weighted Overlap Dominancemodel can suitably satisfy these axioms.In the seventh and last contribution to the Special Session, Multi-criteria Decision Anal-ysis based on Hesitant Fuzzy Linguistic Term Sets: application in Photovoltaic TechnologiesAssessment, Sellak et al. investigate the paradigm of linguistic multi-criteria decision-making.The authors present an outranking method for multi-criteria decision-making, in which decisionmakers use hesitant fuzzy linguistic term sets to qualitatively express their preferences undervarying degrees of uncertainty. The proposed solution alleviates the difficulties that frequentlyarise when comparing and ranking preferential information expressed linguistically, thus leadingto accurate computational processes with understandable decision results. An application in theassessment of solar photovoltaic technologies is provided.We hope that the outcome of this Special Session constitutes a notable contribution in thefield, to continue fostering joint research efforts and addressing the new challenges of effectivelymanaging uncertainty in complex decision support domains. The Special Session Organizerswould like to acknowledge all the contributors for their effort in submitting high quality papers,the reviewers for their generous willingness to evaluate the contributions received, and the con-ference Special Session Chairs, Drs. Susana Montes and Yasuo Kudo, for their assistance duringthe organization of this scientific event.Iva´n Palomares CarrascosaVan-Nam Huynh

Uncertainty Handling in Recommender and Decision Support Systems:Preface to IFSA-SCIS 2017 Special Session 02

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Uncertainty Handling in Recommender and Decision Support Systems:Preface to IFSA-SCIS 2017 Special Session 02

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