Continuous sign language recognition (CSLR) technology enables social communication for the hearing-impaired by converting sign language videos into text. However, due to the limited receptive fields of convolutional networks and inefficient long-range dependency modeling in temporal modules, current methods find it difficult to capture cross-regional and high-order dynamic semantics in complex gestures. To address these limitations, we propose a dynamic spatiotemporal hypergraph network named HyperSign, which optimizes feature learning through innovative graph architectures. For single-frame spatial modeling, we propose a saliency-aware spatial graph construction strategy that dynamically quantifies semantic saliency by integrating feature complexity and motion intensity information from patches. This strategy can adaptively adjust node connectivity based on the computed saliency, thereby enabling the graph structure to focus on information-dense regions such as hands and faces. For temporal dependency modeling, we abandon the conventional pairwise frame interactions and propose a temporal hypergraph construction method. This method employs a learnable clustering algorithm to aggregate semantically correlated nodes within temporal windows into hyperedges, thereby explicitly capturing high-order associations within individual gesture actions that span multiple frames. Extensive experiments on the PHOENIX14, PHOENIX14-T, and CSL-Daily datasets demonstrate that HyperSign outperforms the state-of-the-art (SOTA) approaches in CSLR without any additional annotation information, establishing a new feature learning paradigm for the CSLR task.

Conversational recommender systems (CRSs) aim to mine user preferences and recommend appropriate items through natural language dialogue. A complete CRS typically consists of a recommendation module and a conversation module, which generate high-quality recommended items and fluent natural language responses, respectively. Existing research usually constructs and trains the two modules separately, leading to inconsistencies in input information and construction methods for different subtasks. To address the limitations of the above methods, we propose a joint training framework based on knowledge-aware prompt tuning to build an integrated conversational recommender system (KPICRS). First, we adopt contrastive learning to align the semantic space of the embeddings of the conversation context and the knowledge graph entity, and then generate a prompt template as the unified input of the joint training framework through the prompt encoder. Specifically, we incorporate augmented similar user representations into the prompt template, which helps to alleviate the data sparsity and cold-start problems. Next, we jointly train the pretrained language model (PLM) and the prompt encoder so that the PLM can simultaneously generate predictions for both conversation subtask and recommendation subtask. Extensive experiments on two public English and Chinese CRS datasets demonstrate that our model achieves highly competitive performance.

Multibehavior collaborative filtering recommendations can significantly alleviate data sparsity issues caused by insufficient single-behavior information, enhancing recommendation performance. However, current multibehavior recommendation methods simply concatenate different behavior representations without further exploring the interactive information between behaviors, thus limiting recommendation effectiveness. To address the limitations, we propose the graph contrastive learning for multibehavior recommendation (GCMR) model. First, we use a shared bottom to capture the connections between different behaviors of each user. Then, we introduce a GCN-based multibehavior contrastive learning approach that employs cross-layer and cross-behavior contrastive learning to capture intrabehavior and cross-behavior network interaction information, which enhances user and item representations. Additionally, we propose a multibehavior feature fusion strategy that integrates user representations (and item representations) to fully exploit latent information of different behaviors and improve network representation performance. Extensive experiments on three open-source datasets demonstrate that the GCMR model outperforms the state-of-the-art, especially on the Tmall dataset, where GCMR achieved an improvement of 19.10% in HR@10 and 16.50% in NDCG@10 over the best baseline.

GNN-based social recommendation aims to use social network information to improve recommendation performance of traditional user–item interaction network (U–I network). However, in graph neural network information aggregation, both social networks and U–I networks inevitably have noise, which affects accuracy of recommendation results. To reduce the noise impact of network data, we propose Robust Social Recommendation based on Contrastive Learning and Dual-Stage Graph Neural Network (CLDS). First, considering instability of social networks, we propose the social preference network. It is robust and retains only social friend relationships with common preferences. Based on it and U–I network, we construct a social recommendation pre-training model. Next, we propose self-contrastive learning method. The method initializes multiple social network node representations through Gaussian distribution , pre-training and random disturbance, respectively. Then, it uses contrastive learning on the generated multiple node representations to enhance the robustness of node representation. Finally, CLDS avoids directly capturing potentially user–user and item–item information in U–I networks which is incomplete and untrusted. And instead, it only extracts user–item information to reduce the noise generated by GNN-based U–I network information aggregation. We conduct experiments under the open-source real network dataset. The experimental results show that CLDS outperforms state-of-art methods in social recommendation. The code is available at: https://github.com/Andrewsama/CLDS-master .