To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and future directions. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, multi-domain recommendation, dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising research directions in CDR.
翻译:为解决建议系统(RSs)中长期存在的数据广度问题,提出了跨领域建议(CDR),以利用较富裕领域较丰富的信息,改善较稀少领域的推荐绩效。虽然近年来对CDR进行了广泛研究,但对现有CDR方法缺乏系统审查。为填补这一空白,本文件全面审查了现有的CDR方法,包括挑战、研究进展和未来方向。具体地说,我们首先将现有的CDR方法归纳为四种类型,包括单一目标CDR、多领域建议、双重目标CDR和多目标CDR。然后,我们介绍了这些CDR方法的定义和挑战。接下来,我们建议对这些方法进行全面分类和新的分类,并详细报告其研究进展。最后,我们在CDR中分享了若干有希望的研究方向。