Fuzzy contrastive learning for online behavior analysis

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) latent representation of those contrastive samples, while the semi-supervised learning is then implemented to optimize the fuzzy encoder and minimize the training loss simultaneously. The advantage of the proposed algorithm includes no requirement of domain knowledge, preserving the transparency and interpretability of result, and computational effectiveness. Experimental results, based on a real-world app usage dataset, demonstrate that the applicability and flexibility of the proposed algorithm, compared with other state-of-the-art methods.