Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

该研究提出了一种基于修改的随机游走方法，用于解决现有个性化推荐系统中存在的信息孤立问题，以促进各类不同的推荐内容，特别着眼于针对社交媒体中政治内容的推荐建议问题，该方法不仅能高精度地重建意识形态，而且具有更好的信息多样性和适用性。

擦除随机游走：在社交和信息网络中实现多样化的个性化推荐