Learning from How Users Interpret: Personalizing Recommendation Explanation Order

원문 초록

Personalized explanations typically adapt their content or presentation based on preferences, interaction histories, or other user characteristics. We examine whether regularities inferred from prior textual interpretations can support personalization of explanation organization. In a controlled within participant study with 122 participants, prior interpretations were represented along three dimensions: information selection, relative salience, and relational organization. The relational dimension was partially operationalized through co mobilization. These representations were aggregated into fixed historical profiles and used to organize otherwise identical explanation sentences. Participants encountered four organization procedures: the OWN historical profile, the DONOR historical profile, a population profile called POP, and a language model procedure using the historical profile called PROFILE LLM. Post exposure objective comprehension was the primary outcome. OWN achieved 86.5 percent correct responses, compared with 66.0 percent for DONOR, 63.9 percent for POP, and 73.0 percent for PROFILE LLM. Mixed effects analyses showed higher comprehension with OWN than with each alternative procedure. No reliable overall condition effect was detected for recommendation acceptance. These findings indicate that historical profiles derived from prior interpretations can provide an actionable signal for personalizing explanation order while keeping explanatory content and wording fixed.