重构“接下来看什么”推荐以减少激进化路径
Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
AI总结：
本文将“接下来看什么”推荐建模为 d-正则有向图，通过重构少量推荐边降低激进化内容的隔离分数，并提出基于吸收随机游走的贪心算法。
摘要：
推荐系统通常会向用户推荐与其过去消费内容相似的内容。如果用户碰巧接触到高度极化的内容，她随后可能会收到一些推荐，这些推荐可能会引导她走向越来越激进化的内容，最终陷入我们所称的“激进化路径”。本文研究利用基于图的方法缓解激进化路径的问题。具体而言，我们将“接下来看什么”推荐器的推荐集合建模为一个 d-正则有向图，其中节点对应内容项，链接对应推荐，路径对应可能的用户会话。我们将代表激进化内容的节点的“隔离”分数衡量为从该节点到任何代表非激进化内容节点的随机游走期望长度。高隔离分数与使用户陷入激进化路径的更大可能性相关。因此，我们定义了通过选择少量边进行“重构”来减少激进化路径普遍性的问题，以最小化所有激进化节点中隔离分数的最大值，同时保持推荐的相关性。我们证明，寻找待重构推荐的最优集合这一问题是 NP-hard 的，并且在任何因子内进行近似也是 NP-hard 的。因此，我们转而关注启发式方法，并基于吸收随机游走理论提出了一种高效且有效的贪心算法。我们在视频和新闻推荐情境下的真实数据集上进行的实验证实了所提方法的有效性。
英文摘要：
Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequently receive recommendations which may steer her towards more and more radicalized content, eventually being trapped in what we call a "radicalization pathway". In this paper, we study the problem of mitigating radicalization pathways using a graph-based approach. Specifically, we model the set of recommendations of a "what-to-watch-next" recommender as a d-regular directed graph where nodes correspond to content items, links to recommendations, and paths to possible user sessions. We measure the "segregation" score of a node representing radicalized content as the expected length of a random walk from that node to any node representing non-radicalized content. High segregation scores are associated to larger chances to get users trapped in radicalization pathways. Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations. We prove that the problem of finding the optimal set of recommendations to rewire is NP-hard and NP-hard to approximate within any factor. Therefore, we turn our attention to heuristics, and propose an efficient yet effective greedy algorithm based on the absorbing random walk theory. Our experiments on real-world datasets in the context of video and news recommendations confirm the effectiveness of our proposal.
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