description-based post-hoc explanation for twitter list recommendations

Twitter list recommender systems can generate highly accurate recommendations, but since they employ heterogeneous information of users and lists and apply sophisticated prediction models, they may not provide easy understandable intrinsic explanations. to address this limitation, twitter list descriptions can play a critical role in providing post-hoc explanations that help users make informed decisions. in this paper, we present a model to provide relevant and informative explanations for recommended twitter lists by automatically generating descriptions for them. the model selects the most informative tweets from a list as its description to inform users more with the recommended list that positively contributes to the user experience. more specifically, the explanation model incorporates three categories of features: content relevance features, tweet-specific features and publisher’s authority features that are used in a learning to rank model to rank the list’s tweets in terms of their informativeness. experimental results on a twitter dataset validate the effectiveness of our proposed model in generating useful explanations for recommended twitter lists.