Twitter List recommender systems have achieved high prediction accuracy by leveraging diverse user and List information alongside complex algorithms. However, explainability remains a significant challenge in these systems. Providing meaningful explanations along with a set of recommendations can enhance user trust and satisfaction, assisting them in informed decision-making. In this paper, we present a model for the automated generation of personalized descriptions as explanations for recommended Twitter Lists. Specifically, our model selects frequently used hashtags from the content of the recommended List, establishing semantic relationships with the user\\\\\\\'s activity history. The aim is to present the List\\\\\\\'s subject in an understandable and personalized manner through a generated description. Through experiments conducted on a real Twitter dataset, our proposed model demonstrates its capability to generate explanations for a high percentage of the recommendations provided by a recommendation model.

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