The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining neighborhoods, collecting similar users, calculating product reputation based on similar users, and recommending high-reputation products. To overcome the complexity of customer behaviors and traditional clustering methods, an unsupervised clustering approach based on product categories is developed to enhance the recommendation methodology. This study makes notable contributions in several aspects. Firstly, a groundbreaking behavior-based recommendation methodology is developed, incorporating customer behavior to generate accurate and tailored recommendations leading to improved customer satisfaction and engagement. Secondly, an original unsupervised clustering method, focusing on product categories, enables more precise clustering and facilitates accurate recommendations. Finally, an approach to determine neighborhoods for active customers within clusters is established, ensuring grouping of customers with similar behavioral patterns to enhance recommendation accuracy and relevance. The proposed recommendation methodology and clustering method contribute to improved recommendation performance, offering valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Additionally, the proposed method outperforms benchmark methods in experiments conducted using a behavior dataset from the well-known e-commerce site Alibaba.

中文翻译：

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一种新颖的基于行为的电子商务推荐系统

大多数现有推荐系统依赖于用户评分，但由于缺乏用户协作和稀疏问题而受到限制。为了解决这些问题，本研究提出了一种基于行为的推荐系统，该系统利用客户在电子商务平台上的自然行为，例如浏览和点击。所提出的推荐系统包括对活跃客户进行聚类、确定邻域、收集相似用户、基于相似用户计算产品声誉以及推荐高声誉产品。为了克服客户行为和传统聚类方法的复杂性，开发了一种基于产品类别的无监督聚类方法来增强推荐方法。这项研究在几个方面做出了显着的贡献。首先，开发了一种突破性的基于行为的推荐方法，结合客户行为来生成准确且量身定制的推荐，从而提高客户满意度和参与度。其次，独创无监督聚类方法，聚焦产品类别，聚类更精准，有利于精准推荐。最后，建立了一种确定集群内活跃客户邻域的方法，确保对具有相似行为模式的客户进行分组，以提高推荐的准确性和相关性。所提出的推荐方法和聚类方法有助于提高推荐性能，为电子商务推荐系统领域的研究人员和从业者提供有价值的见解。此外，在使用知名电子商务网站阿里巴巴的行为数据集进行的实验中，所提出的方法优于基准方法。