ITMPRec: Intention-based Targeted Multi-round Proactive Recommendation

Personalized recommendations are integrated into daily life, but providers may want certain items to become more appealing over time through user interactions, yet this issue is often overlooked. The existing works are often based on the assumption that users will passively accept all intermediate sequences or not explore intention modeling in the targeted nudging process. Both of these factors result in suboptimal performance in the proactive recommendation. In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. We first select target items using a pre-match strategy. Then, we employ a multi-round nudging recommendation method, incorporating a module to quantify users' intention-level evolution, helping choose suitable intermediate items. Additionally, we model users' sensitivity to changes caused by these items. Lastly, we propose an LLM agent as a pluggable component to simulate user feedback, offering an alternative to traditional click models by leveraging the agent's external knowledge and reasoning capabilities. Through extensive experiments on four public datasets, we demonstrate the superiority of ITMPRec compared to eight baseline models.