Knowledgeable Explanations for Recommender Systems

By Markus Zanker, Daniela Ninaus. 2010

40 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “allocation (LDA) for recommendation, and Zhang et al. [2014a] formally defined the explainable recommendation problem, and proposed the Explicit Factor Model (EFM) by aligning the latent dimensions with explicit product features for explainable recommendation.”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “Usefulness Ease of use Match score + decisive features (Zanker and Ninaus 2010) Match score + decisive features (Zanker 2012)”
Make it personal: A social explanation system applied to group recommendations (2017, background): “, 2000): 1) showing an explicative text giving a reason or justification for (Zanker and Ninaus, 2010; Tintarev and Masthoff, 2012) or 2) using images to enhance the credibility of information (Sharma and Cosley, 2013; Fogg et al.”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “How explanations can also increase beliefs in the competence and benevolence of a system [54] and the perceived usefulness of a system [55].”
AliCoCo: Alibaba E-commerce Cognitive Concept Net (2020, background): “e 2(c). While explainable recommendation attracts much research attention recently [33], most existing works are not practical enough for industrysystems,sincetheyareeithertoocomplicated(based on NLG [8, 32]), or too trivial (e.g., “how many people also viewed” [9, 17]). Our proposed concepts, on th”