Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead learning processes, reducing both recommendation accuracy and platform value. Existing denoising strategies typically overlook the entity-specific nature of noise while introducing high computational costs and complex hyperparameter tuning. To address these challenges, we propose EARD (Entity-Aware Reliability-Driven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability. Motivated by the empirical observation that training loss correlates with noise, EARD quantifies user and item reliability via their average training losses as a proxy for reputation, and integrates these entity-level factors with interaction-level confidence. The framework is model-agnostic, computationally efficient, and requires only two intuitive hyperparameters. Extensive experiments across multiple datasets and backbone models demonstrate that EARD yields substantial improvements over state-of-the-art baselines (e.g., up to 27.01% gain in NDCG@50), while incurring negligible additional computational cost. Comprehensive ablation studies and mechanism analyses further confirm EARD's robustness to hyperparameter choices and its practical scalability. These results highlight the importance of entity-aware reliability modeling for denoising implicit feedback and pave the way for more robust recommendation research.

Large language models (LLMs) have demonstrated exceptional error detection capabilities and can correct sentences with high fluency in grammatical error correction (GEC) tasks. However, when correcting Chinese academic papers, LLMs face significant challenges of over-correction. To delve deeper into this issue, we explore the underlying reasons. On one hand, each discipline has its unique vocabulary and expressions, and LLMs have insufficient and incomplete understanding of domain-specific sentences. On the other hand, the controllability of generative LLMs in GEC tasks is inherently poor, and the traditional sequence-to-sequence (Seq2Seq) correction structure exacerbates this issue. Considering the two aforementioned factors, we propose a new error correction framework for Chinese academic GEC tasks using LLMs, named ScholarGEC. To improve LLMs’ understanding of domain-specific knowledge, we construct appropriate disciplinary knowledge prefixes for sentences and use this domain-specific knowledge data to fine-tune the LLM. To enhance the controllability of LLMs, we replace the traditional Seq2Seq structure with a Detection-Correction separated structure. We also introduce a special token during the process to improve the model’s error detection stability. Additionally, we incorporate iterative self-reflection to enhance the stability of the generation, in the three parts of LLM generation. Extensive experiments demonstrate the effectiveness and robustness of our framework on a Chinese GEC dataset composed of academic papers, and further analysis reveals the capabilities of our framework in enhancing LLM performance in general GEC tasks.

Although previous studies have applied diffusion models to time series forecasting, these efforts have struggled to preserve the intrinsic temporal correlations within the series, leading to suboptimal predictive outcomes. This failure primarily results from the introduction of independent, identically distributed (i.i.d.) noise. In the forward process, the addition of i.i.d. noise to the time series gradually diminishes these temporal correlations. The reverse process starts with i.i.d. noise and lacks priors related to temporal correlations, which can result in directional biases during sampling. From a frequency-domain perspective, noise disrupts the low-frequency-dominated structure of trend components, making it difficult for the model to learn long-term temporal dependencies. To address these limitations, we introduce a decomposition prediction framework to complement the novel Temporal Correlation-Empowered Diffusion Model. Overall, We decompose the time series into trend and residual components, predict them using a base model and a diffusion model, and then combine the results. Specifically, a frequency-domain MLP model was adopted as the base model due to its not distorting the original sequence, and better the capture of long-range temporal dependencies. The diffusion model incorporates two key modules to capture short- and mid-range temporal correlations: the Maintaining Temporal Correlation Module and the Redesigned Initial Module. Extensive experiments across multiple datasets demonstrate that the proposed method significantly outperforms related strong baselines.

In the realm of recommender systems, accurately predicting Click-Through Rate (CTR) is a critical task that involves learning user-item interaction features. Many researchers propose novel models to mine interaction signals, but they neglect that redundancy itself causes high computational cost and leads to suboptimal performance. Some tried to remove redundancy by dropping useless features, or shrinking the size of embedding table. However, current feature selection methods are vulnerable to training stochasticity and data dynamics, while embedding size assignment techniques neglect the importance relationships between feature fields. The simple combination of the two optimization ways will also yield poor performance due to the inherent gap in their optimization targets. Hence, there is no effective paradigm that can optimize feature fields from the two aspects in a simultaneous and coordinated way. In this paper, we identify the core issue as the lack of a practical score to measure the contribution of feature fields, and propose a distribution-based field optimization framework that adopts importance distribution to provide a comprehensive view for both methods. We innovatively design a learner for each field to acquire the stable and comprehensive importance situation. Then, based on this, we eliminate noise features, and assign adaptive embedding sizes for different feature fields according to the similarity of importance. With this field optimization, our proposed framework has extremely low pre-training overhead, greatly reduces training and inference time, and even achieves more accurate prediction results with fewer feature fields.

With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of "one-size-fits-all." To break this limitation and boost user engagement, in this paper, we introduce PCG (**P**ersonalized Visual **C**ontent **G**eneration), a unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personalization and the fidelity of generated images. Specifically, an LLM-powered Inclinations Analyzer is adopted to capture user likes and dislikes from context to construct personalized prompts. Moreover, we design a dual-stage LoRA mechanism—Global LoRA for understanding task-specific visual style, and Local LoRA for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images. Extensive experiments on benchmark conversational datasets—including objective metrics and GPT-based evaluations—demonstrate that our framework outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for conversational scenarios like e-commerce and real-world recommendation.