01782nas a2200229   4500000000100000000000100001008004100002260001200043653002800055653001100083653002000094653002700114100002500141700002900166700002600195245012800221856008000349300001200429490000600441520109100447022001401538       2023                            d  c06/202310aArtificial Intelligence10aGraphs10aLink Prediction10aRecommendation Systems1 aMarta Caro-Martínez1 aGuillermo Jiménez-Díaz1 aJuan A. Recio-García00aLocal Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_19.pdf  a202-2120 v83 aExplanations in recommender systems are a requirement to improve users’ trust and experience. Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles. However, this information is not available in black-box recommender systems that lack sufficient data transparency. This current work proposes a local model-agnostic, explanation-by-example method for recommender systems based on knowledge graphs to leverage this knowledge requirement. It only requires information about the interactions between users and items. Through the proper transformation of these knowledge graphs into item-based and user-based structures, link prediction techniques are applied to find similarities between the nodes and to identify explanatory items for the user’s recommendation. Experimental evaluation demonstrates that these knowledge graphs are more effective than classical content-based explanation approaches but have lower information requirements, making them more suitable for black-box recommender systems.  a1989-1660