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Extracting information and knowledge from users’ online activity is of great significance for a variety of practical purposes. Yet, existing research suffers from limitations including requiring prior knowledge and poor interpretability. In this study, we develop a novel classification algorithm based on the dual concepts of fuzzy set and sparsity regularization. Specifically, the proposed algorithm introduces two types of fuzzy sets designed to fuzzify samples, then the membership criteria from resultant fuzzy sets is further cast as the soft feature for training a sparse classifier. To demonstrate the practical benefits of this process and the performance of the proposed classification algorithm, we carefully examine its application to several benchmarking datasets, in addition to a unique real-world data resource containing 49,252 users worldwide and their 55,539 online historical records collected over a ten-month period. Experimental results demonstrate that the proposed algorit
With the prevalence of smart devices, billions of people are accessing digital resource in their daily life. Online user-behavior modeling, as such, has been actively researched in recent years. However, due to the data uncertainty (sparse-ness and skewness), traditional techniques suffer from certain drawbacks, such as relying on labor-intensive expertise or prior knowledge, lacking of interpretability and transparency, and expensive computational cost. As a step toward bridging the gap, this paper proposes a fuzzy-set based contrastive learning algorithm. The general idea is to design an end-to-end learning framework of optimizing representation from contrastive samples. The proposed algorithm is characterized by three main modules, including data augmentation, fuzzy encoder, and semi-supervised optimization. More precisely, data augmentation is used to produce contrastive (positive and negative) samples based on anchor ones. The fuzzy encoder is introduced to fuzzify (or encode) lat