Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with the evolution of users' interests, detrimentally affecting the target users' experience. To avoid this issue, we propose a new task named Proactive Recommendation in Social Networks (PRSN) that indirectly steers users' interest by utilizing the influence of social neighbors, i.e., indirect steering by adjusting the exposure of a target item to target users' neighbors. The key to PRSN lies in answering an interventional question: what would a target user' s feedback be on a target item if the item is exposed to the user' s different neighbors? To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item' s exposure to the user' s neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience. To this end, we propose a Neighbor Interference Recommendation (NIRec) framework with two modules: (1) an interference representation-based estimation module for modeling potential feedback; (2) a post-learning-based optimization module for adjusting a target item' s exposure to trade off steering performance and the neighbors' experience through greedy search. We conduct extensive semi-simulation experiments on real-world datasets, validating the steering effectiveness of NIRec.

翻译：仅推荐迎合用户历史兴趣的内容会限制用户的视野。近期研究尝试通过直接调整向目标用户展示的内容来引导其超越历史兴趣。然而，直接引导所推荐的内容可能无法完全契合用户兴趣的演变过程，从而对目标用户体验产生负面影响。为避免这一问题，我们提出了一项名为"社交网络主动推荐"的新任务，该任务通过利用社交邻居的影响力间接引导用户兴趣，即通过调整目标内容在目标用户邻居中的曝光度进行间接引导。PRSN的关键在于回答一个干预性问题：若将目标内容曝光给目标用户的不同邻居，该用户对目标内容的反馈将如何变化？为回答此问题，我们借助因果推断将PRSN形式化为两个子问题：(1)在目标内容对用户邻居产生网络干扰的条件下，估计用户对该内容的潜在反馈；(2)调整目标内容在目标用户邻居中的曝光度，以权衡引导效果与对邻居体验的损害。为此，我们提出了包含双模块的邻居干扰推荐框架：其一是基于干扰表征的估计模块，用于建模潜在反馈；其二是基于后学习的优化模块，通过贪心搜索调整目标内容曝光度以平衡引导效果与邻居体验。我们在真实数据集上进行了大量半仿真实验，验证了NIRec框架在兴趣引导方面的有效性。