ITMPRec: Intention-based Targeted Multi-round Proactive Recommendation

Yahong Lian, Chunyao Song, Tingjian Ge

Track: User modeling, personalization and recommendation

Keywords: sequential recommendation, proactive recommendation, intention, LLM

Abstract:

Personalized user preference driven recommendations have seamlessly intertwined with our daily lives. However, item providers may expect specific items to gradually increase their appeal to users over the course of users’ long-term interactions with the system, but few studies pay attention to this problem. In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. Specifically, we first choose a set of target items from the target category, by conducting a pre-match strategy. Afterward, we utilize a multi-round nudging recommendation method, in which we design a module to quantify the intention-level dynamic evolution of users so that we could choose more appropriate intermediate items during guidance. Besides, we model each user’s sensitivity to the changes in representation induced by the intermediate items they accept. Finally, we propose a design for a Large Language Model (LLM)
agent as a pluggable component to simulate user feedback. This design offers an alternative to the traditional click model based on distribution, relying on the agent’s external knowledge and reasoning capabilities. Through extensive experiments on four public datasets, we demonstrate the superiority of ITMPRec compared to seven baseline models. The code repository is available at https://anonymous.4open.science/r/ITMPRec-D821.

Submission Number: 1300