Proactive Recommendation in Social Networks: Steering user interest with causal inference

Abstract

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. The code is available at https://github.com/HungPaan/NIRec.