Leveraging LLMs to Explain the Consequences of Recommendations

Recommender systems help users make better decisions by suggesting products that match their preferences.However, users often do not understand why certain products are recommended, which can reduce trust and satisfaction.While explanations address this issue, they often fail to communicate the individual impact the decision for an item will have.To address this, we present an LLM-based framework for generating consequence-based explanations.These explanations provide comprehensible personalized insights into the positive and negative consequences of user decisions.To support the assessment and selection of the most effective prompting strategy, we introduce evaluation metrics tailored to consequenceaware explanations and systematically compare different prompting strategies for an apartment recommendation example.