跨领域推荐方法研究综述

A review of cross-domain recommendation methods

近年来,随着信息技术的迅速发展的爆发性增长,这一爆发式增长推动了跨领域推荐系统的出现和发展。跨领域推荐系统的设计和实现面临着诸多挑战,包括数据异构性、领域知识融合等问题。因此,书写跨领域推荐方法的研究变得尤为重要。这些方法旨在有效地整合来自不同领域的数据和信息,同时保持推荐系统的高效性和准确性。为实现这一目标,研究者们提出了各种跨领域推荐方法,包括基于迁移学习方法、基于多任务学习的方法等跨领域推荐方法,文章将从处理步骤及优缺点梳理各跨领域推荐系统方法。

In recent years,with the explosive growth of the rapid development of information technology,this explosive growth has promoted the emergence and development of cross-field recommendation systems.The design and implementation of cross-domain recommendation systems face many challenges,including data heterogeneity and domain knowledge fusion.Therefore,the study of writing cross-field recommendation methods has become particularly important.These methods are designed to effectively integrate data and information from different domains while maintaining the efficiency and accuracy of recommender systems.In order to achieve this goal,researchers propose a variety of cross-domain recommendation methods,including transfer-based learning methods,multi-task learning-based methods and other cross-domain recommendation methods.