用于对话式推荐的显式知识图谱推理

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen, Lizhen Cui, Zi Huang, Hongzhi Yin
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引用次数: 0

摘要

传统的推荐系统纯粹根据历史交互记录来估算用户对项目的偏好,因此无法捕捉细粒度但动态的用户兴趣,用户只能被动地接受推荐。最近的对话式推荐系统(CRS)解决了这些局限性,它使推荐系统能够与用户互动,通过一连串的澄清问题获得用户当前的偏好。最近,在会话推荐系统中使用知识图谱(KGs)的做法开始兴起,其核心动机是将知识图谱所携带的丰富侧边信息纳入推荐和会话过程。然而,现有的基于知识图谱的 CRS 有两个缺陷:(1) 语篇的学习表示与知识图谱实体之间存在语义鸿沟,阻碍了相关知识图谱信息的检索;(2) 对知识图谱的推理大多是通过隐式学习的用户兴趣来进行的,忽略了对话中实际提及的实体的显式信号。为了解决这些问题,我们提出了一种新的会话推理框架,即知识增强会话推理(KECR)模型。由于用户可以通过属性级和项目级表达来反映自己的偏好,因此 KECR 将两个级别的结构化知识共同嵌入到 KG 中。为实现语篇嵌入空间与 KG 实体之间的语义对齐,进一步提出了互信息最大化约束。同时,KECR 利用 KG 内部的连通性对用户需求进行显式推理,使模型不再依赖于用户对澄清问题的反馈。因此,语义对齐和明确的 KG 推理可以共同促进准确的推荐和高质量的对话生成。通过在两个真实世界数据集上与强大的基线进行比较,我们证明了 KECR 获得了最先进的推荐效果以及具有竞争力的对话生成性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation.

To address these drawbacks, we propose a new CRS framework, namely the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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