Sequential recommendation (SR) has leveraged the advantages of graph contrastive learning (GCL) to enhance the representation of SR, which mitigates to some extent the constraint of scarce labeled data for supervision in SR. Existing work applies general graph data augmentation strategies to generate positive sample pairs, then further representation learning is conducted through a shared graph neural network. In this study, we identify limitations in applying traditional GCL to sequential recommendation: after the data augmentation, the shared graph neural network architecture used for feature learning fails to supply sufficiently diverse contrastive views, which are necessary to effectively identify and focus on the key information that is truly relevant for sequential recommendation. To ease this limitation, we propose a novel framework named Model Augmented Graph Contrastive Learning for Sequential Recommendation (MA-GCL4SR), which emphasizes modifying the internal architectures of the graph neural network through the use of model augmentation strategies, rather than focusing on making improvements during the data augmentation phase before encoding. Thereby, we construct a non-shared view encoder for SR, enriching the samples of user’s interaction sequences and strengthen the stability of the augmented sequence. Extensive experiments on four real-world datasets confirm the effectiveness of the proposed MA-GCL4SR paradigm, showcasing its consistent ability to elevate model performance across various real-world scenarios.

API recommendation differs from conventional recommendation systems in that it integrates the characteristics of development requirements, mashup, and API, where there are two primary challenges, i.e., how to effectively mine the personalized development requirements of developers and the sparseness of interaction data. This research proposes a novel model framework, called Bayesian Probabilistic Matrix Factorization Model with Text Similarity and Adversarial Training (SAMF), to address these two issues. Utilizing natural language processing technology to extract the full-text semantics of requirements documents, mashup descriptions, and API descriptions, fully mining personalized development requirements, and contextual information of mashups and APIs, and calculating text similarity, is our fundamental idea. Simultaneously, the collaborative filtering method is used to mine user needs, mashup, and API information from historical data, and the obtained text similarity is added to the training as auxiliary information. Furthermore, adversarial training is further incorporated to supplement data in order to minimize data sparsity and enhance model robustness and generalization. To assess the performance of SAMF, we run thorough experiments that illustrate the efficacy of each module of the model and explain the influence of hyperparameter settings. Specifically, compared to the baseline, the experimental findings demonstrate that SAMF can basically achieve better performance.