推荐系统信任网络中的协同社会度量学习

IF 4.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal on Semantic Web and Information Systems Pub Date : 2023-01-20 DOI:10.4018/ijswis.316535
Taehan Kim, Wonzoo Chung
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引用次数: 0

摘要

本文提出了一种新的top-K排序推荐方法——协同社会度量学习(CSML),该方法实现了一个结构简单的用户-物品和用户-用户交互的信任网络。大多数采用信任网络的现有推荐系统都关注于项目评级,但这并不总是保证最优的top-K排名预测。信任网络中传统的直接排序系统是基于次最优关联方法,不考虑项目间的关系。CSML算法利用度量学习方法直接预测信任网络中的top-K项。进一步提出了一种新的三重损失,称为社会中心损失,它表示用户-用户交互以充分利用信任网络中包含的信息,作为推荐系统度量学习中考虑用户-物品和物品-物品关系的两种常用三重损失的补充。实验结果表明,所提出的CSML在真实信任网络数据上优于现有的推荐系统。
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Collaborative Social Metric Learning in Trust Network for Recommender Systems
In this study, a novel top-K ranking recommendation method called collaborative social metric learning (CSML) is proposed, which implements a trust network that provides both user-item and user-user interactions in simple structure. Most existing recommender systems adopting trust networks focus on item ratings, but this does not always guarantee optimal top-K ranking prediction. Conventional direct ranking systems in trust networks are based on sub-optimal correlation approaches that do not consider item-item relations. The proposed CSML algorithm utilizes the metric learning method to directly predict the top-K items in a trust network. A new triplet loss is further proposed, called socio-centric loss, which represents user-user interactions to fully exploit the information contained in a trust network, as an addition to the two commonly used triplet losses in metric learning for recommender systems, which consider user-item and item-item relations. Experimental results demonstrate that the proposed CSML outperformed existing recommender systems for real-world trust network data.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
审稿时长
20 months
期刊介绍: The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.
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