English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Despite the acknowledgment that the perception of explanations may varyconsiderably between end-users, explainable recommender systems (RS) havetraditionally followed a one-size-fits-all model, whereby the same explanationlevel of detail is provided to each user, without taking into considerationindividual user's context, i.e., goals and personal characteristics. To fillthis research gap, we aim in this paper at a shift from a one-size-fits-all toa personalized approach to explainable recommendation by giving users agency indeciding which explanation they would like to see. We developed a transparentRecommendation and Interest Modeling Application (RIMA) that provides on-demandpersonalized explanations of the recommendations, with three levels of detail(basic, intermediate, advanced) to meet the demands of different types ofend-users. We conducted a within-subject study (N=31) to investigate therelationship between user's personal characteristics and the explanation levelof detail, and the effects of these two variables on the perception of theexplainable RS with regard to different explanation goals. Our results showthat the perception of explainable RS with different levels of detail isaffected to different degrees by the explanation goal and user type.Consequently, we suggested some theoretical and design guidelines to supportthe systematic design of explanatory interfaces in RS tailored to the user'scontext.

Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System