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Explainable Recommendation: ASurvey and New PerspectivesYongfeng Zhang1and Xu Chen21Rutgers University, USA; University, China; recommendation attempts to develop modelsthat generate not only high-quality recommendations butalso intuitive explanations. The explanations may either bepost-hoc or directly come from an explainable model (alsocalled interpretable or transparent model in some contexts).Explainable recommendation tries to address the problemofwhy: by providing explanations to users or system design-ers, it helps humans to understand why certain items arerecommended by the algorithm, where the human can eitherbe users or system designers. Explainable recommendationhelps to improve the transparency, persuasiveness, effective-ness, trustworthiness, and satisfaction of recommendationsystems.

1 Introduction 1.1ExplainableRecommendation Inthissection,wewillintroducethedefinitionoftheexplainablerecom-mendationproblemitself.Wewillhighlightthepositionofexplainable

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