The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions about network structure (like homophily) and group cognition (like, social action is largely imitative). Assumptions like these can be na "ive and exclusionary in the era of fake news and ideological uniformity towards the political poles. We examine these assumptions with aid from the user-centric framework of trustworthiness in social recommendation. The constituent dimensions of trustworthiness (diversity, transparency, explainability, disruption) highlight new opportunities for discouraging dogmatization and building decision-aware, transparent news recommender systems.
Research increasingly indicates that accuracy cannot be the sole criteria in creating a satisfying recommender from the users' point of view. Other criteria, such as diversity, are emerging as important characteristics for consideration as well. In this paper, we try to address the problem of augmenting users' perception of recommendation diversity by applying an organization interface design method to the commonly used list interface. An in-depth user study was conducted to compare an organization interface with a standard list interface. Our results show that the organization interface indeed effectively increased users' perceived diversity of recommendations, especially perceived categorical diversity. Furthermore, 65 of users preferred the organization interface, versus 20 for the list interface. 70 of users thought the organization interface is better at helping them perceive recommendation diversity versus only 15 for the list interface. In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.