Sabiri Bihi, Khtira Amal, El Asri Bouchra, Rhanoui Maryem
IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat 10130, Morocco.
LASTIMI Laboratory, EST Salé, Mohammed V University in Rabat, Salé 11060, Morocco.
J Imaging. 2025 Jan 7;11(1):12. doi: 10.3390/jimaging11010012.
As technology develops, consumer behavior and how people search for what they want are constantly evolving. Online shopping has fundamentally changed the e-commerce industry. Although there are more products available than ever before, only a small portion of them are noticed; as a result, a few items gain disproportionate attention. Recommender systems can help to increase the visibility of lesser-known products. Major technology businesses have adopted these technologies as essential offerings, resulting in better user experiences and more sales. As a result, recommender systems have achieved considerable economic, social, and global advancements. Companies are improving their algorithms with hybrid techniques that combine more recommendation methodologies as these systems are a major research focus. This review provides a thorough examination of several hybrid models by combining ideas from the current research and emphasizing their practical uses, strengths, and limits. The review identifies special problems and opportunities for designing and implementing hybrid recommender systems by focusing on the unique aspects of big data, notably volume, velocity, and variety. Adhering to the Cochrane Handbook and the principles developed by Kitchenham and Charters guarantees that the assessment process is transparent and high in quality. The current aim is to conduct a systematic review of several recent developments in the area of hybrid recommender systems. The study covers the state of the art of the relevant research over the last four years regarding four knowledge bases (ACM, Google Scholar, Scopus, and Springer), as well as all Web of Science articles regardless of their date of publication. This study employs ASReview, an open-source application that uses active learning to help academics filter literature efficiently. This study aims to assess the progress achieved in the field of hybrid recommender systems to identify frequently used recommender approaches, explore the technical context, highlight gaps in the existing research, and position our future research in relation to the current studies.
随着技术的发展，消费者行为以及人们搜索所需物品的方式也在不断演变。在线购物从根本上改变了电子商务行业。尽管现在可供选择的产品比以往任何时候都多，但只有一小部分能被注意到；结果，少数产品获得了不成比例的关注。推荐系统有助于提高不太知名产品的可见度。各大科技企业已将这些技术作为核心产品采用，从而带来了更好的用户体验并增加了销售额。因此，推荐系统在经济、社会和全球层面都取得了显著进展。随着这些系统成为主要研究重点，各公司正在用结合了更多推荐方法的混合技术改进其算法。本综述通过结合当前研究的思路并强调其实际用途、优势和局限性，对几种混合模型进行了全面考察。该综述通过关注大数据的独特方面，特别是体量、速度和多样性，确定了设计和实施混合推荐系统的特殊问题和机遇。遵循Cochrane手册以及Kitchenham和Charters制定的原则，可确保评估过程透明且质量高。当前的目标是对混合推荐系统领域的几项最新进展进行系统综述。该研究涵盖了过去四年中关于四个知识库（ACM、谷歌学术、Scopus和Springer）的相关研究的最新状态，以及所有Web of Science文章，无论其发表日期如何。本研究采用ASReview，这是一款开源应用程序，利用主动学习帮助学者高效筛选文献。本研究旨在评估混合推荐系统领域所取得的进展，以确定常用的推荐方法，探索技术背景，突出现有研究中的差距，并将我们未来的研究与当前研究相关联。
