Stochastic Embeddings in Multi-Intent Contrastive Learning for Sequential Recommendation

Sequential recommendation (SR) predicts the next item by analyzing users' historical interaction data.However, it may lead to the homogenization of recommended items due to reliance on historical data.User sequences may also reflect latent intents influenced by factors such as seasonality or age.To address these challenges, we propose a model named Stochastic Embeddings in Multi-Intent Contrastive Learning for Sequential Recommendation (SICLR).SICLR uses mean and covariance embeddings to represent stochastic embeddings of sequential intents, leveraging Wasserstein distance to measure the distance between distributions.This approach can capture the uncertainty and non-stationarity in the distributions of intents.In addition, SICLR employs a multi-perspective of local and global viewpoints for intent contrastive learning, which enhances the capture of latent intent discrepancies and interest drifts.Furthermore, SICLR also integrates the recommendation task with local and global intent contrastive learning tasks.This can improve the recommendation performance of the model.The proposed model demonstrates improved performance metrics across three realworld datasets.It shows robustness and generalization under different training parameters.