%20based%20recommender%20system%20by%20encoding%20user%20interactions%20on%20item%20catalogs.%20Our%20study%20explores%20whether%20network-level%20metrics%20on%20KGs%20can%20influence%20the%20degree%20of%20surprise%20in%20recommendations.%20We%20hypothesize%20that%20surprisingness%20correlates%20with%20certain%20network%20metrics%2C%20treating%20user%20profiles%20as%20subgraphs%20within%20a%20larger%20catalog%20KG.%20The%20achieved%20solution%20reranks%20recommendations%20based%20on%20their%20impact%20on%20structural%20graph%20metrics.%20Our%20research%20contributes%20to%20optimizing%20recommendations%20to%20reflect%20the%20metrics.%20We%20experimentally%20evaluate%20our%20approach%20on%20two%20datasets%20of%20LastFM%20listening%20histories%20and%20synthetic%20Netflix%20viewing%20profiles.%20We%20find%20that%20reranking%20items%20based%20on%20complex%20network%20metrics%20leads%20to%20a%20more%20unexpected%20and%20surprising%20composition%20of%20recommendation%20lists.)
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