  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.
