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 complex prediction models, they cannot 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 propose an explanation model to provide relevant and informative explanations for recommended Lists by automatically generating descriptions for Twitter Lists. 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. By conducting experiments on a Twitter dataset, we have shown that the proposed model provides useful explanations for the Lists that are recommended to users, while upholding parity in recommendation performance.