利用自适应邻图聚合的图注意网络进行冷启动推荐

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2024-09-18 DOI:10.1007/s10844-024-00888-3
Qian Hu, Lei Tan, Daofu Gong, Yan Li, Wenjuan Bu
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引用次数: 0

摘要

冷启动问题是推荐系统中一个长期存在的问题,即缺乏历史交互信息会阻碍对新用户和新项目的有效推荐。现有方法通常会结合用户和项目的属性信息来解决严格的冷启动问题。大多数现有的推荐方法都忽略了冷启动推荐系统中用户属性的稀缺性。在本文中,我们开发了一个新颖的框架--用于冷启动推荐的自适应邻接图聚合图注意力网络(A-GAR),它利用冷启动推荐系统中的用户/物品关系信息来缓解属性稀疏性问题。我们可以利用图结构充分探索用户/物品之间的复杂关系,从而在冷启动场景中实现更准确的推荐。具体来说,为了学习用户/物品属性之间的复杂关系,我们利用 SENet(挤压和激励网络)和 MLP(多层感知器)网络来自适应地融合用户/物品的嵌入向量及其二阶交互向量,从而实现高阶特征聚合。为了解决冷启动推荐中缺乏偏好信息的问题,我们扩展了变异自动编码器,从用户/物品的高阶属性特征中重建缺失的用户偏好(物品特征)。为了学习邻居图结构中节点的潜在语义关系,我们使用了一个属性图注意力网络来聚合用户的邻居信息和邻居之间的交互信息。这样,节点之间的高阶关系和相邻图的潜在语义就能被充分挖掘出来。在三个具有不同冷启动场景的实词数据集上进行的广泛实验表明,A-GAR 对严格的冷启动推荐有显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Graph attention networks with adaptive neighbor graph aggregation for cold-start recommendation

The cold-start problem is a long-standing problem in recommender systems, i.e., lack of historical interaction information hinders effective recommendations for new users and items. Existing methods typically incorporate attribute information of users and items to address the strict cold-start problem. Most existing recommendation methods overlook the sparsity of user attributes in cold start recommendation systems. In this paper, we develop a novel framework, Graph Attention Networks with Adaptive Neighbor Graph Aggregation for cold-start Recommendation (A-GAR), which utilizes the user/item relationship information in cold-start recommendation systems to alleviate the sparsity of attributes. we can achieve more accurate recommendations in cold-start scenarios by fully exploring the complex relations between users/items using graph structures. Specifically, to learn the complex relationships between user/item attributes, we utilize SENet (Squeeze and Excitation Network) and MLP (Multilayer Perceptron) networks to adaptively fuse the embeddings of user/item and their second-order interaction vectors, achieving high-order feature aggregation. To address the issue of lacking preference information in cold-start recommendations, we extend the variational autoencoder to reconstruct missing user preferences (item characteristics) from higher-order attribute features of users/items. In order to learn the potential semantic relationships of nodes in the neighbor graph structure, an attribute graph attention network is used to aggregate the neighbor information of users and the interaction information between neighbors. In this way, the high-order relationships between nodes and the potential semantics of adjacent graphs can be fully explored. Extensive experiments on three real-word datasets with various cold-start scenarios demonstrate that A-GAR yields significant improvements for strict cold-start recommendations.

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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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