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 .

Recommender systems aim at helping users to discover interesting items and assisting business owners to obtain more profits. Nonetheless, traditional recommendations fail to explore the varying importance of product characteristics for different product domains. In light of this, we propose a novel probabilistic model for recommendation, which could learn products’ characteristics in a fine-grained manner. Specifically, a user’s preference for a given product is modeled as a combination of visual and functional aspects. To make our method practical in large-scale industrial scenarios, we devise a computationally efficient learning algorithm to optimize VFPMF’s parameters. Experiments on four real-world datasets demonstrate the effectiveness and efficiency of our solution, compared with several state-of-the-art methods.

An effective recommender system can significantly help customers to find desired products and assist business owners to earn more income. Nevertheless, the decision-making process of users is highly complex, not only dependent on the personality and preference of a user, but also complicated by the characteristics of a specific product. For example, for products of different domains (e.g., clothing versus office products), the product aspects that affect a user’s decision are very different. As such, traditional collaborative filtering methods that model only user-item interaction data would deliver unsatisfactory recommendation results. In this work, we focus on fine-grained modeling of product characteristics to improve recommendation quality. Specifically, we first divide a product’s characteristics into visual and functional aspects—i.e., thevisual appearanceandfunctionalityof the product. One insight is that, the visual characteristic is very important for products of visually-aware domain (e.g., clothing), while the functional characteristic plays a more crucial role for visually non-aware domain (e.g., office products). We then contribute a novel probabilistic model, namedVisual and Functional Probabilistic Matrix Factorization(VFPMF), to unify the two factors to estimate user preferences on products. Nevertheless, such an expressive model poses efficiency challenge in parameter learning from implicit feedback. To address the technical challenge, we devise a computationally efficient learning algorithm based on alternating least squares. Furthermore, we provide an online updating procedure of the algorithm, shedding some light on how to adapt our method to real-world recommendation scenario where data continuously streams in. Extensive experiments on four real-word datasets demonstrate the effectiveness of our method with both offline and online protocols.

Traditional image clustering algorithms deal with single-task clustering (STC) problem on a single domain. However, with the increasing number of related images on the Web, it is challenging for STCs to perform related image clustering tasks independently without considering the between-task relationship, which mainly consists of similar visual features and image patterns among tasks. Therefore, it is intuitive to resort to multi-task clustering (MTC) algorithms. However, most existing MTCs learn a shared feature subspace, which may lead to negative transfer when facing the image clustering tasks that are not strongly related. In this paper, we propose a novel multi-task image clustering algorithm, which performs multiple image clustering tasks simultaneously and propagates the task correlation to improve clustering performance. Specifically, we first extend the information bottleneck method to cluster tasks independently. The related and unrelated images between the pairwise clusters of different tasks are then discovered. Meanwhile, two corresponding types of correlations are propagated among the tasks, where only the positive correlation benefits the clustering of each task. A sequential and collaborative method is further designed to ensure an optimal solution. Moreover, we perform a theoretical analysis of the properties on correlation propagation and the convergence of our algorithm. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art clustering methods.

Existing visual-textual cross-modal clustering techniques focus on finding a clustering partition of different modalities by dealing with each modality dependently or integrating multiple modalities into a shared space, which may results in unsatisfactory performance due to the heterogeneous gap of different modalities. Aiming at this problem, we propose a novel heterogeneous dual-task clustering (HDC) method, which is capable of exploring high-level relatedness between visual and textual data to improve the performance of individual task. Our intuition is that although the visual and textual data are heterogenous to each other, they may share related high-level semantics and rich latent correlations, which can lead to improved performance if we treat the clustering of visual and textual data as different but related learning tasks. Specifically, the problem of heterogeneous dual-task clustering is formulated as an information-theoretic function, in which the low-level information in each modality and high-level relatedness between multiple modalities are maximally preserved. Then, a progressive optimization method is proposed to ensure a local optimal solution. Extensive experiments show noticeable performance of the HDC approach in comparison with several state-of-the-art baselines.

The widespread adoption of low-cost cameras generates massive amounts of videos recorded from different viewpoints every day. To cope with this vast amount of unlabeled and heterogeneous data, a new multi-task information bottleneck co-clustering (MIBC) approach is proposed to automatically categorize human actions in collections of unlabeled cross-view videos. Our motivation is that, if a learning action category from each view is seen as a single task, it is reasonable to assume that the tasks of learning action patterns from the videos recorded by multiple cameras are dependent and inter-related, since the actions of the same subjects synchronously recorded from different camera viewpoints are complementary to each other. MIBC aims to transfer the shared view knowledge across multiple tasks (i.e., camera viewpoints) to boost the performance of each task. Specifically, MIBC involves the following two parts: (1) extracting action categories for each task by independently maintaining its own relevant information, and (2) allowing the feature representations of all tasks to be compressed into a common feature space, which is utilized to capture the relatedness of multiple tasks and transfer the shared knowledge across different camera viewpoints. These two parts of MIBC work simultaneously and can be solved in a novel co-clustering mechanism. Our experimental evaluation on several cross-view action collections shows that the MIBC algorithm outperforms the existing state-of-the-art baselines.

Proximal support vector machine (PSVM) is a simple but effective classifier, especially for solving large-scale data classification problems. An inherent deficiency of PSVM lies on its inefficiency for dealing with high-dimensional data. In this paper, we propose a parallel version of PSVM (PPSVM). Based on random dimensionality partitioning, PPSVM can obtain partitioned local model parameters in parallel, with combined parameters to form the final global solution. In fact, PPSVM enjoys two properties: 1) It can calculate model parameters in parallel and is therefore a fast learning method with theoretically proved convergence; and 2) It can avoid the inversion of large matrix, which makes it suitable for high-dimensional data. In the paper, we also propose a random PPSVM with randomly partitioned data in each iteration to improve the performance of PSVM. Experimental results on real-world data demonstrate that the proposed methods can obtain similar or even better prediction accuracy than PSVM with much better runtime efficiency.

Active learning traditionally assumes that labeled and unlabeled samples are subject to the same distributions and the goal of an active learner is to label the most informative unlabeled samples. In reality, situations may exist that we may not have unlabeled samples from the same domain as the labeled samples (i.e. target domain), whereas samples from auxiliary domains might be available. Under such situations, an interesting question is whether an active learner can actively label samples from auxiliary domains to benefit the target domain. In this paper, we propose a transfer active learning method, namely Transfer Active SVM (TrAcSVM), which uses a limited number of target instances to iteratively discover and label informative auxiliary instances. TrAcSVM employs an extended sigmoid function as instance weight updating approach to adjust the models for prediction of (newly arrived) target data. Experimental results on real-world data sets demonstrate that TrAcSVM obtains better efficiency and prediction accuracy than its peers.