In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's performance. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to contrast the valid information with the noisy signal in the data, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to support their ideas or motivations. We aim to alleviate this situation by proposing a citation recommendation strategy that recalls related papers for a rough idea (a piece of text, i.e., abstract, manuscript). However, the perspective of existing citation recommendations can not be well applied to our task for two defects. First, these methods neglect the reasoning of research topics, which makes the recommendation mechanism not meticulous enough and lacks explainability. For instance, they are not able to mine the hidden citing logic for the candidate paper while recommending. We fill the research gap by constructing structural topics consisting of knowledge concepts from the textual content, where reasoning paths between topics are extracted from an external knowledge graph. Second, the citation network is viewed as a crucial structural context to enhance the recommendation performance, but the new target idea does not have links to the citation network as published papers do. To simulate the prospective topological structure, our model, meanwhile, incorporates a contrastive-learning-based alignment paradigm to encourage the consistency of content embeddings and structure-oriented embeddings. We evaluate our proposed model on three real-world datasets and demonstrate that it significantly improves recommendation accuracy while providing high-quality knowledge-aware reasoning. And an interesting visual example illustrates the reasoning process when our model actually judges samples, which supports the feasibility of our topic-view learning paradigm.

Background and Objective: Mainstream image synthesis methods fail to capture local contextual information long-range dependence, and adaptability, especially under the influence of computation overload and random noise distribution, leading to the loss of contrast and detailed information. Approach: To alleviate these issues, we propose a Generative Adversarial Network aggregating large kernel decomposable attention (LKDA-GAN) bottleneck block for cross-modality image synthesis. Initially, a novel LKDA module is proposed by combining a spatial local convolution, a spatial long-range convolution, and a channel convolution, which aims to balance the local contextual information and the long-range dependence, and enhance the channel adaptability by enlarging the receptive field. Subsequently, a bottleneck block is designed and integrated into the LKDA module by feature dimensional transformation to capture rich semantic information and alleviate computational overhead. Ultimately, an auxiliary registration network (ARN) based on the noise transition matrix is put forward to learn the prior knowledge of the noise distribution and produce the unique optimal solution. Furthermore, it is based on Res-UNet to avoid network degradation. Main results: By analyzing qualitative assessment and quantitative measurement , extensive experiments demonstrate the proposed approach outperforms the state-of-the-art methods, and ablation studies also show the superiority of LKDA. Significance: LKDA-GAN provides a suitable way to synthesize images between different modalities, which is conductive to indicate disease areas and improve diagnosis accuracy.