Cold-start recommendation is a well-known problem in practical application scenarios. Generating reliable recommendations can be challenging when interactions are typically sparse. To mitigate the cold-start problem, some methods incorporate auxiliary information about users and items, and others adopt meta-learning to improve recommendation accuracy. However, these approaches overlook the fact that items are interdependent and likely to be related or similar. Moreover, user preference distributions in the meta-training and meta-testing phases are different in the cold-start scenario. To address these problems, we present a novel strategy called Causal Meta-learning with Multi-view Graphs (CausalMMG). Specifically, we first construct multi-view item-item graphs to explore the correlations and similarities between items from multiple perspectives. A multi-view item representer is then used to learn item representations, exploiting graph convolution neural networks to capture the structure of these different item–item graphs. We then resort to the structural causal models of causal inference and further develop a causality-enhanced bi-level adaptive meta-learner to eliminate bias caused by the different distributions of user preferences. Moreover, the meta-learner learns the user preferences for items in different orders through hierarchical and task-level adaptations. Finally, we evaluate CausalMMG on several real-world datasets, demonstrating its effectiveness in various scenarios. The results show that the proposed CausalMMG is significantly superior to competitive baseline methods for cold-start recommendation on all datasets, highlighting the importance of incorporating the multiple relationships between items and modeling different user preference distributions in recommender systems.

Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is utilized to determine the optimal timing for requesting attribute information or suggesting items. However, existing methods consider user-preferred attributes independently and ignore that attributes may be of different importance to the same user, in the attribute and item selection phases, which limits the accuracy and interpretability of CRS. Inspired by this, we propose deep conversational path reasoning (DeepCPR), which involves constructing a reasoning path on a graph with a series of user-favored attributes. It utilizes the attention mechanism to thoroughly examine the connections between these attributes and provide improved explanations for which attributes to inquire about or which items to recommend. In DeepCPR, two deep-learning-based modules are proposed to realize attribute and item selection. In the first module, the sequence of attributes confirmed by the user in conversation is encoded with a gated graph neural network to obtain the user’s long-term preference using a self-attention mechanism for the selection of candidate attributes. In the second module, a self-attention approach with more appropriate strategies is developed to dynamically select candidate items. In addition, to achieve fine-grained user preference modeling, a recurrent neural network is employed to aggregate the sequence of attributes that interact with the users. Numerous experimental evaluations conducted on four real CRS datasets show that the proposed method significantly outperforms existing advanced methods in terms of conversational recommendations.

Zero-shot image recognition attempts to simulate the zero-shot learning mechanism of humans and recognizes the images of novel classes. It is crucial to learn transferable knowledge from seen classes and generalize it to unseen classes for image recognition in zero-shot learning (ZSL). Most existing ZSL methods extract visual features with pretrained backbone networks and learn transferable knowledge with the extracted visual features. However, the backbone networks are not pretrained for a special task, and the extracted visual features usually contain some distractive information for the ZSL task, which causes some discriminative information to be ignored or weakened and degrades the quality of knowledge learned from seen classes. Moreover, since visual samples of unseen classes are not obtainable, domain shift is another challenging problem. In this paper, we propose visual feature enhancement to learn more discriminative visual features via a graph convolutional network (GCN) and an attention mechanism for improving the quality of the learned transferable knowledge. Different from previous works, we explore the correlations between different latent visual patterns of an image and introduce GCN to enhance visual features. On the other hand, we take advantage of different learning mechanisms of GCN and MLP and propose dual classifier learning for improving the generalization and inference capabilities of our model. In end-to-end model training, the module of visual feature enhancement and the module of dual classifier learning are beneficial to each other via joint optimization. Finally, we perform extensive experiments in the ZSL setting and GZSL setting. The extensive experimental results verify the effectiveness and superiority of our method.