用大模型规划推荐路径，帮用户跳出信息茧房
Leveraging LLMs for Influence Path Planning in Proactive Recommendation
- 用大模型理解用户兴趣变化和物品特性，生成连贯推荐序列
- 新方法使推荐路径接受度提升，且更符合用户兴趣演变逻辑
- 适合做主动推荐、个性化内容引导的场景
推荐系统在社交平台中至关重要，但通常仅基于用户历史兴趣，易导致信息茧房。为拓展用户视野，主动推荐系统旨在通过影响路径（即一系列推荐项序列）逐步引导用户关注目标项。现有方法如影响力推荐系统（IRS）存在目标项未被包含和路径不连贯的问题。本文提出基于大语言模型的影响力路径规划（LLM-IPP），利用大模型的规划能力，结合用户兴趣迁移与物品特征，生成连贯且有效的推荐路径。我们设计了新的评估指标与用户模拟器，对比传统方法进行基准测试。实验表明，LLM-IPP 显著提升用户接受度与路径一致性，优于现有方法。
原文摘要 · Abstract (English)
Recommender systems are pivotal in Internet social platforms, yet they often cater to users' historical interests, leading to critical issues like echo chambers. To broaden user horizons, proactive recommender systems aim to guide user interest to gradually like a target item beyond historical interests through an influence path,i.e., a sequence of recommended items. As a representative, Influential Recommender System (IRS) designs a sequential model for influence path planning but faces issues of lacking target item inclusion and path coherence. To address the issues, we leverage the advanced planning capabilities of Large Language Models (LLMs) and propose an LLM-based Influence Path Planning (LLM-IPP) method. LLM-IPP generates coherent and effective influence paths by capturing user interest shifts and item characteristics. We introduce novel evaluation metrics and user simulators to benchmark LLM-IPP against traditional methods. Our experiments demonstrate that LLM-IPP significantly enhances user acceptability and path coherence, outperforming existing approaches.
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