Data Augmentation Integrating User Preferences for Sequential Recommendation

In order to effectively alleviate the data sparsity issue, the application of contrastive learning in sequential recommendation is studied. To address the problem of noise introduced by random data augmentation, the data augmentation method incorporating user preferences is proposed. This method guides the augmentation process through user ratings to generate augmented sequences that align with user preferences. Then, the traditional sequence prediction objectives are combined with contrastive learning objectives to extract more meaningful user patterns and further encode the user representation effectively. In addition, experimental verification is performed on datasets Beauty, Toys and Sports. Compared with the best result in the baseline model, our method averagely improved by 7.26%, 2.05% and 2.24% on three datasets, respectively. The above experimental results have verified the rationality and validity of the model.