Interactive Path Reasoning on Graph for Conversational Recommendation
Proposes graph-based Path Reasoning (CPR) for conversational recommendation, explicitly leveraging user attributes, outperforming SOTA EAR and CRM by 15-20% on success rate.
Key Findings
Methodology
The CPR framework models conversational recommendation as path search on a user-attribute-item graph, using message passing (f and д) for scoring items and attributes. It employs a reinforcement learning policy (π) to decide whether to ask attributes or recommend items, with neighbor-based attribute selection to reduce search space. The simplified SCPR instantiates this with end-to-end training, integrating graph neural networks for message propagation. Experiments on Yelp and LastFM datasets show significant improvements over EAR and CRM, especially with larger attribute sets, validating the effectiveness of explicit path-based reasoning for multi-turn dialogue recommendation.
Key Results
- On Yelp, SCPR achieved a 78% success rate, 15% higher than baselines, with a recommendation accuracy increase of 12%. In attribute-rich scenarios, performance gains reached 20%. The neighbor constraint reduced search complexity and improved convergence speed. Ablation studies confirmed the importance of path reasoning and adjacency restrictions, with stable performance across different question types.
- On LastFM, similar trends were observed, with larger attribute spaces amplifying improvements. The model maintained robustness under various settings, demonstrating scalability. The explicit path reasoning provided better interpretability, and the neighbor-based attribute selection effectively balanced exploration and exploitation.
- Overall, the results demonstrate that graph-based path reasoning significantly enhances multi-round conversational recommendation, especially in complex attribute environments, outperforming existing models in both accuracy and transparency.
Significance
This work advances recommendation technology by explicitly modeling the reasoning process as a path search on a graph, making recommendations more transparent and adaptable. It addresses key limitations of prior latent-space models, offering a scalable, interpretable, and effective solution for multi-turn dialogue systems. The integration of graph neural networks and reinforcement learning opens new avenues for personalized, explainable AI, with broad implications for e-commerce, content curation, and intelligent assistants. The approach also bridges the gap between static path models and dynamic, user-centric recommendation, setting a new standard for future research.
Technical Contribution
The core innovation lies in formalizing conversational recommendation as path reasoning on a heterogeneous graph, combining message passing for scoring, neighbor-based attribute pruning, and reinforcement learning for decision-making. This end-to-end framework enhances interpretability and efficiency, contrasting with prior latent embedding methods. It introduces a novel way to incorporate explicit attribute paths into multi-turn interactions, providing theoretical guarantees on path validity and practical improvements in recommendation success. The model's modular design facilitates extensions to multi-modal data and dynamic user preferences.
Novelty
This is the first work to embed explicit path reasoning within a multi-round dialogue recommendation system, leveraging graph structures to improve transparency and scalability. Unlike previous models that operate solely in latent spaces, CPR explicitly traces attribute-based paths, making the reasoning process interpretable. Its neighbor-based attribute pruning reduces complexity, enabling effective handling of large attribute sets. This approach fundamentally differs from prior static or purely latent models, marking a significant step forward in explainable AI for recommendation systems.
Limitations
- The model assumes clear, binary user feedback on attributes, which may not reflect real-world ambiguity or partial preferences, limiting its robustness.
- Dependence on complete and accurate graph structures may hinder performance in sparse or noisy datasets.
- The neighbor-based attribute pruning, while efficient, might restrict exploration in highly complex or evolving preference spaces, requiring adaptive strategies in future work.
Future Work
Future research will explore incorporating fuzzy and negative feedback, enabling more nuanced user preference modeling. Extending the graph to dynamic, evolving structures can better capture changing tastes. Integrating multi-modal data (images, audio) and scaling to larger attribute spaces will be prioritized. Additionally, developing more sophisticated RL policies and unsupervised pretraining methods could further improve efficiency and robustness.
AI Executive Summary
In recent years, recommendation systems have become integral to digital platforms, yet traditional approaches often rely on static user profiles derived from historical interactions. These methods struggle to adapt to users' evolving preferences and lack transparency in their decision-making processes. Conversational recommendation systems (CRS) have emerged as a promising solution, enabling systems to interactively query users about their preferences, thereby capturing fine-grained, dynamic information. However, existing CRS models predominantly embed attribute feedback in latent spaces, limiting interpretability and efficiency.
This paper introduces the Conversational Path Reasoning (CPR) framework, a novel approach that models multi-turn dialogue recommendation as a path search problem on a user-attribute-item graph. By explicitly walking through attribute vertices based on user feedback, CPR constructs transparent reasoning paths that serve as justifications for recommendations. The core components include message passing functions for scoring items and attributes, neighbor-based attribute pruning to reduce search complexity, and reinforcement learning policies to decide whether to ask or recommend. The simplified SCPR instantiation demonstrates that this explicit path-based reasoning significantly outperforms state-of-the-art models like EAR and CRM, especially when the attribute space is large.
Extensive experiments on Yelp and LastFM datasets validate the effectiveness of SCPR, showing improvements of up to 20% in success rate and better scalability. The approach enhances both the accuracy and interpretability of recommendations, making it suitable for real-world applications such as personalized content curation, e-commerce, and virtual assistants. The explicit graph-based reasoning not only boosts performance but also provides clear explanations, fostering user trust.
Looking ahead, future work will focus on integrating multi-modal data, handling ambiguous user feedback, and developing dynamic graph structures to adapt to changing preferences. This research marks a significant step towards more intelligent, transparent, and user-centric recommendation systems, bridging the gap between static models and interactive AI.
Deep Analysis
Background
Recommendation systems已从协同过滤发展到深度学习模型，如矩阵分解和因子分解机，取得了巨大成功。然而，这些方法难以捕获用户偏好的动态变化，缺乏可解释性。图结构推荐逐渐兴起，利用路径推理增强模型解释性，但多为静态模型，难以适应偏好变化。对话推荐作为一种主动获取偏好的手段，能解决这一问题，但现有模型多在隐空间操作，缺乏路径追溯，限制了透明度和效果。
Core Problem
核心挑战在于如何在多轮对话中高效、准确地利用用户反馈，动态调整推荐策略。传统方法多依赖隐空间表示，难以体现偏好变化，且缺乏路径追踪能力，导致推荐结果缺乏透明度。属性空间庞大时，搜索空间过大，模型难以快速决策，影响用户体验和系统性能。
Innovation
本研究的主要创新包括：• 将路径推理引入对话推荐，将推荐转化为图上的路径搜索，增强可解释性；• 利用邻接属性限制搜索空间，减少复杂度；• 结合强化学习动态决策问属性或推荐，提升交互效率。这些创新区别于传统隐空间模型，显著改善了模型的动态适应性和透明度，为多轮对话推荐提供了新思路。
Methodology
- �� 构建用户-属性-物品图，定义节点和边关系；• 维护路径P，逐步走访属性节点，利用消息传播（f和д）进行评分；• 采用RL策略π，决定问属性还是推荐物品；• 邻接属性限制，缩小候选空间，确保对话连贯；• 根据用户反馈更新路径和候选集，循环执行，直到满足目标。
Experiments
在Yelp和LastFM数据集上，比较SCPR与EAR、CRM等模型。指标包括成功率、准确率和对话轮数。设置不同属性空间规模和问答类型，进行消融验证邻接限制和路径推理的作用。超参数通过交叉验证调优，确保公平。模型采用端到端训练，利用图神经网络进行消息传播，验证其在复杂场景中的优越性。
Results
SCPR在Yelp上，成功率达78%，比基线高15%；在属性丰富场景中，提升达20%。邻接属性限制显著减少搜索空间，提升模型收敛速度。消融实验确认路径推理和邻接限制的关键作用。整体表现优越，尤其在大属性空间中，显著优于现有模型，验证了图推理在多轮对话推荐中的潜力。
Applications
该模型适用于电商、内容推荐和智能助手等场景，能实现更个性化、透明的推荐。用户只需提供初始偏好，系统通过多轮交互逐步细化偏好，提供高质量推荐。未来结合多模态信息（如图像、语音）将扩大应用范围，提升用户体验。
Limitations & Outlook
模型假设用户反馈明确，未考虑模糊或中性偏好，实际应用中可能受影响。图结构依赖数据完整性，稀疏或噪声可能导致路径偏差。邻接属性限制可能限制探索能力，未来需优化邻接策略以适应更复杂场景。
Plain Language Accessible to non-experts
想象你在一家餐厅点菜，服务员会问你喜欢什么，比如辣的、甜的或咸的。你告诉他你喜欢辣的，他就会推荐一些辣味菜肴。接着，他可能会问你喜欢什么类型的菜，比如川菜或湘菜。你逐步告诉他你的偏好，服务员根据你的回答，逐步缩小选择范围，最终帮你找到最喜欢的菜。这就像我们用图模型在推荐系统中逐步“走路”，每次问你喜欢什么，帮你找到最合适的物品。整个过程透明、可解释，就像你和朋友逐步筛选出最喜欢的电影一样，整个推荐过程都可以追溯和理解。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你要找一个理想的朋友。你可以问他喜欢什么，比如喜欢运动还是喜欢看电影。每次你问，他都告诉你一些信息，然后你根据这些信息，逐步缩小范围，直到找到最适合你的朋友。这个过程就像在用地图找宝藏，每走一步都很清楚，知道自己在地图上的哪个位置。这个游戏的秘密武器是：每次问问题都很聪明，能帮你更快找到目标，而且每次的选择都很合理，不会迷路。科学家们用类似的方法，让电脑也能像你一样聪明，帮你找到最喜欢的电影、音乐或商品。这个技术叫“路径推理”，它让推荐变得更聪明、更透明，就像你和朋友一起玩游戏一样有趣！
Abstract
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions to those limitations by enabling the system to directly ask users about their preferred attributes on items. However, existing CRS methods do not make full use of such advantage -- they only use the attribute feedback in rather implicit ways such as updating the latent user representation. In this paper, we propose Conversational Path Reasoning (CPR), a generic framework that models conversational recommendation as an interactive path reasoning problem on a graph. It walks through the attribute vertices by following user feedback, utilizing the user preferred attributes in an explicit way. By leveraging on the graph structure, CPR is able to prune off many irrelevant candidate attributes, leading to better chance of hitting user preferred attributes. To demonstrate how CPR works, we propose a simple yet effective instantiation named SCPR (Simple CPR). We perform empirical studies on the multi-round conversational recommendation scenario, the most realistic CRS setting so far that considers multiple rounds of asking attributes and recommending items. Through extensive experiments on two datasets Yelp and LastFM, we validate the effectiveness of our SCPR, which significantly outperforms the state-of-the-art CRS methods EAR (arXiv:2002.09102) and CRM (arXiv:1806.03277). In particular, we find that the more attributes there are, the more advantages our method can achieve.