Knowledge-aware recommendation leverages rich item-related factual information in Knowledge Graphs (KGs) to enhance recommendation systems. However, most existing methods focus on developing complex models to extract information from a given KG. They essentially follow a model-centric paradigm, overlooking data quality problems. In practice, KG data exhibits two principal quality problems, namely the noisy knowledge problem and the incomplete knowledge problem, which severely impair the performance of downstream models. To address these problems, we adopt a data-centric paradigm to improve the quality of KG data. Inspired by diffusion models' superior denoising and generation ability by fitting true data distributions, we propose a novel spectral heterogeneous diffusion framework for knowledge-aware recommendation. This framework tailors a diffusion model to capture the recommendation-oriented heterogeneous distribution in the original KG and then converts the fitted distribution into a high-quality KG. Specifically, we design a spectral heterogeneous diffusion model that integrates recommendation prior knowledge to capture task-relevant distribution and aligns its diffusion process with the features of heterogeneous graphs to model heterogeneity. Furthermore, we propose a continuous-discrete mode adapter that transforms the learned continuous distribution into a high-quality discrete KG. The resulting KG is denoised and enriched with task-relevant triples, mitigating noisy and incomplete knowledge problems. Experiments show that our plug-and-play framework can be integrated with any knowledge-aware recommendation model and boost their performance by improving KG quality. The code and theoretical analyses are available at https://github.com/xiangmli/SHGD.

• Plang: A language blending natural prompt with control flow for precise LLM guidance. • Meta-prompt programming: Enables LLM to self-modify prompt programs during execution. • 40.75%-89.55% conciseness gain: Outperforms methods like LangChain in prompt coding. • Open-source solution: Enables multi-agent collaboration & tool use with minimal code. The advent of instruction-following large language models (LLMs), exemplified by ChatGPT, has significantly enhanced the performance of generative autoregressive natural language models on general tasks, marking a crucial milestone toward artificial general intelligence. While research has shown that LLM performance critically depends on prompt effectiveness, existing prompt construction approaches - including prompt string templates, prompt programming frameworks, and prompt programming syntactic sugar - suffer from limitations in imprecise generation control and deviation from natural language syntax, thereby impeding prompt engineering advancement. To address these challenges, we introduce PromptLanguage (Plang), a string-first programming language designed specifically for LLM prompt engineering. Our key innovation lies in utilizing font styles as syntax keywords to seamlessly integrate natural language prompt text with control flow code, enabling precise intervention in the generation process while maintaining the natural language affinity of prompt programs. Notably, Plang pioneers the concept of meta-prompt programming. Extensive experimental results across prompt engineering cases and quantitative analyses demonstrate that Plang-written prompt programs offer superior read/writability, intervention precision and up to 89.55% efficiency improvements. The Plang implementation is available as open-source software at https://github.com/HJZ-XDU/plang .

Skeleton sequences for action recognition are with complex temporal dynamics due to various factors such as speed variation and different activities. It is crucial and essential to model variation changes in the temporal dimension. In recent years, skeleton sequence is always modeled as a graph structure, and Graph Convolution Network (GCN) is employed to extract spatial and temporal features of actions. Though GCN has obtained great achievements, they typically employ fixed-size temporal kernels for temporal modeling, which ignore the complex temporal dynamic of actions, especially for long-term as well as short-term modeling. To capture this complex motion pattern effectively, we propose a Dual Multi-Scale Graph Convolutional Network (DMS-GCN), which is mainly composed of a Deformable Temporal Kernel (DTK) block and a dual multi-scale strategy. Specifically, the DTK block is proposed to flexibly capture complex temporal information of the skeleton sequence. And the dual multi-scale strategy is used to simultaneously accommodate long-term and short-term dynamic information at different scales globally as well as locally. The effectiveness of our proposed method is verified through experiments conducted on two widely used datasets, NTU-RGB+D 60 and NTU-RGB+D 120.

Code review is essential for maintenance of pull request-based software systems. Recommending suitable reviewers for code changes can enhance defect detection and knowledge dissemination. Despite extensive research, the inherent complexity of pull requests (PRs) and reviewer profiles continues to cause challenge for accurate matching them together. Furthermore, existing methods often amplify gender and racial/ethnic disparities due to the lack of attention to biases present in historical review records. To address these issues, we first collected a dataset from 4 large-scale open-source projects involving 50 -month revision history, reaching up to 30 attributes. This dataset includes gender and racial/ethnic information, which was inferred, validated, and incorporated to enable comprehensive data bias analysis in reviewer recommendation tasks. Additionally, we introduce a fairness-aware and accurate approach: CoReBM, which leverages the advanced semantic understanding capabilities of Large Language Models (LLMs) to comprehensively capture the nuanced textual context of both PRs and reviewers, utilizing the robust planning, collaborative, and decision-making abilities of multi-agent systems. CoReBM integrates diverse factors to improve recommendation performance while mitigating bias effects through the incorporation of candidates' gender and racial/ethnic attributes. We evaluate the effectiveness of our approach on this dataset, and the results demonstrate that CoReBM outperforms state-of-the-art methods in both accuracy and fairness in recommendation.

Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dual-matching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning. 2) An exercise generation-adversarial mechanism collaboratively refines exercise generation leveraging a group of quality evaluation expert agents via iterative adversarial feedback. Finally, a comprehensive evaluation protocol is carefully designed to assess ExeGen. Extensive experiments on real-world educational datasets and a practical deployment in college education demonstrate the effectiveness and superiority of ExeGen. The code is available at https://github.com/dsz532/exeGen.

Multi-behavior recommendation systems aim to incorporate auxiliary behaviors (e.g., click, cart, etc.) to enhance the understanding of sparse target behaviors (e.g., purchase), thereby capturing user preferences more accurately. Currently, multi-behavior recommendation research focuses on modeling the associations between different user behaviors, but ignores the large amount of noise in user interaction data. This noise may come from accidental touches, curiosity, or ineffective operations during the purchasing process, and can be further categorized into two types: 1) hard noise is significantly deviates from the user's true preferences, and 2) soft noise is closer to the user's true preferences. The presence of noise can interfere with the model's ability to accurately identify the user's true preferences. To overcome the aforementioned issue, we innovatively propose a Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation (DeMBR). The model eliminates different types of noise at the data level and the representation level, respectively. Specifically, since hard noise significantly deviates from user preferences, we design a pruning-based denoising module that leverages a memory bank, which identifies and removes hard noise interactions from the data. Since soft noise reflects some user preferences, we design a semantic guidance denoising module that leverages behaviors with strong expressive ability (e.g., purchase) to guide those with weaker ability (e.g., click), effectively suppressing noise while preserving true's preferences. Finally, we designed a cross-learning module that allows noise-identifying signals to be exchanged between the two modules, and ultimately learn representations that accurately reflect user's preferences. Extensive experiments conducted on two public datasets demonstrate that our model substantially surpasses the state-of-the-art recommendation models. Our code is publicly available at: https://github.com/DeMBR2024/DeMBR.git

Assigning pull requests to appropriate code reviewers can accelerate the review process and help uncover potential bugs. However, the inherent complexities in pull requests and code reviewers present challenges in making suitable matches between them. Prior studies focus on mining rich semantic information from pull requests or profile information from code reviewers to improve efficiency. These approaches often overlook the intrinsic relationships between pull requests and code reviewers, which can be represented by a combination of multiple factors and strategies, resulting in suboptimal recommendation accuracy.