将用户认知意图与因果推理相分离,实现知识增强型推荐

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen
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引用次数: 0

摘要

有效推荐系统的首要目标是提供准确、多样和个性化的推荐,使之与用户的认知意图相一致。知识图谱(KG)能够有效地表示结构和语义信息,因此越来越多地被用来捕捉推荐系统的辅助信息。基于图神经网络(GNN)的知识图谱感知推荐模型的最新进展支持了这一趋势。然而,这些模型经常会遇到一些问题,如用户与项目的交互不足,以及在信息传播过程中用户意图权重不一致。此外,这些模型还面临着流行度偏差的问题,而少数高活跃度用户不成比例的影响力和有限的项目辅助信息又加剧了流行度偏差。这种偏差大大降低了推荐的有效性。为了解决这个问题,我们提出了一种知识增强型用户认知意图网络(KeCAIN),它结合了物品类别信息,通过信息聚合来捕捉用户意图,并消除推荐系统中基于因果推理的流行度偏差。在三个真实世界数据集上的实验表明,KeCAIN 的性能优于最先进的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation

Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation

The primary objective of an effective recommender system is to provide accurate, varied, and personalized recommendations that align with the user’s cognitive intents. Given their ability to represent structural and semantic information effectively, knowledge graphs (KGs) are increasingly being utilized to capture auxiliary information for recommendation systems. This trend is supported by the recent advancements in graph neural network (GNN)-based models for KG-aware recommendations. However, these models often struggle with issues such as insufficient user-item interactions and the misalignment of user intent weights during information propagation. Additionally, they face a popularity bias, which is exacerbated by the disproportionate influence of a small number of highly active users and the limited auxiliary information about items. This bias significantly curtails the effectiveness of the recommendations. To address this issue, we propose a Knowledge-Enhanced User Cognitive Intent Network (KeCAIN), which incorporates item category information to capture user intents with information aggregation and eliminate popularity bias based on causal reasoning in recommendation systems. Experiments on three real-world datasets show that KeCAIN outperforms state-of-the-art baselines.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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