Recently, graph contrastive learning (GCL) has attracted considerable attention in social recommendation, owing to its ability to enhance the robustness of node embedding learning against noise and data sparsity. Despite their effectiveness, we argue that existing GCL-based methods remain limited by three key issues: (1) during graph propagation, they rely on uniform neighbor aggregation and non-adaptive embedding readout, leading to suboptimal node representations; (2) when constructing contrastive views, they typically adopt graph augmentations based on stochastic perturbations of graph-structured data, which may undermine model fidelity; (3) during model optimization, they treat all observed instances equally, forgoing the subtle difference of each positive sample at different training periods. To address these limitations, we propose an effective and efficient GCL framework (EGCL) for social recommendation. Specifically, we devise a graph adaptive propagation module to learn informative embeddings of all items and users. Furthermore, we devise an augmentation-free dual CL paradigm, which consists of intra-CL within a single domain and inter-CL between two separate domains. In addition, we develop a self-adaptive weighted supervised learning paradigm and formulate the whole training procedure as a bi-level optimization problem. Extensive experiments are performed on four benchmarks, demonstrating the effectiveness and efficiency of EGCL over recent state-of-the-art recommenders. Our implementation and datasets are available at https://github.com/wubinzzu/EGCL .

As a key preprocessing step in classification, feature selection involves two conflicting objectives: maximizing the classification accuracy and minimizing the number of selected features. Therefore, multi-objective optimization is widely used in feature selection due to its excellent trade-off between the convergence of two objectives. However, most existing multi-objective feature selection methods still face the issues of the “curse of dimensionality” and high computational costs, especially when the search space is large. To solve the above issues, this paper proposes a bidirectional dynamic grouping multi-objective evolutionary approach for high-dimensional feature selection, referred to as BDGMOEA. This approach transforms a high-dimensional feature selection problem into a feature selection task with a smaller search space by the idea of feature grouping, in which one bit of an individual represents a group of features. Specifically, a grouping search strategy is developed to divide the features into different quadrants according to the importance of the features obtained by different evaluation techniques. Then, the features in each quadrant are grouped by sector. This strategy can effectively narrow the search space and quickly locate promising feature regions. In addition, a bidirectional dynamic adjustment mechanism is presented by considering the evolutionary state of the population, and it can be used to explore each feature in more detail and comprehensively to prevent good features from being ignored in unselected groups. The experimental results demonstrate that the proposed BDGMOEA method performs the best in most cases, indicating that BDGMOEA not only achieves better classification performance but also reduces the training time.

This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front compared to two other multiobjective optimization evolutionary algorithms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 209–226, 2006.