Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs.Previous work focuses on leveraging a user's own history, overlooking inter-user differences that are crucial for effective personalization.While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions.To address these issues, we propose Difference-aware Embeddingbased Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts.DEP constructs soft prompts by contrasting a user's embedding with those of peers who engaged with similar content, highlighting relative behavioral signals.A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM.Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics.

Shape part segmentation is a critical task in computer graphics and robotics. However, traditional supervised methods rely heavily on large amounts of labeled data, which poses significant challenges in many real-world scenarios where such data is often scarce or difficult to obtain. To address this issue, we propose an unsupervised, class-agnostic part segmentation method called Point Diffusion Segmentation (PDS). Our research demonstrates that unconditional point cloud diffusion models can capture abstract object concepts within their sub-attention layers. By extracting preliminary point cloud features from these attention maps, PDS generates efficacious segmentation results. This method fully leverages unlabeled data and proves to be highly applicable in various downstream tasks, including zero-shot part segmentation. Without resorting to any labeled data, PDS improves the zero-shot part segmentation performance of PointClipV2 by 3.1% on the ShapeNet Part dataset, setting a new state-of-the-art baseline and demonstrating significant potential of PDS.

Ensuring fair recommendations for users with different sensitive attributes is essential for building trustworthy recommender systems. A significant challenge in achieving this in the real world is that some users are unwilling to disclose their sensitive attributes, limiting the applicability of traditional approaches. Recent efforts have attempted to address this challenge by reconstructing sensitive attributes based on the observed data. However, the observed data often does not represent an unbiased sample of the true distribution, rendering the reconstructed results unreliable. Moreover, it is difficult to select a debiasing method to achieve unbiased reconstruction, due to lacking sufficient prior knowledge about the bias. This motivates us to develop new fairness approaches.

In 3D skeleton-based action recognition, the limited availability of supervised data has driven interest in self-supervised learning methods. The reconstruction paradigm using masked auto-encoder (MAE) is an effective and mainstream self-supervised learning approach. However, recent studies indicate that MAE models tend to focus on features within a certain frequency range, which may result in the loss of important information. To address this issue, we propose a frequency decoupled MAE. Specifically, by incorporating a scale-specific frequency feature reconstruction module, we delve into leveraging frequency information as a direct and explicit target for reconstruction, which augments the MAE's capability to discern and accurately reproduce diverse frequency attributes within the data. Moreover, in order to address the issue of unstable gradient updates caused by more complex optimization objectives with frequency reconstruction, we introduce a dual-path network combined with an exponential moving average (EMA) parameter updating strategy to guide the model in stabilizing the training process. We have conducted extensive experiments which have demonstrated the effectiveness of the proposed method.

In recent years, remarkable progress has been made in skeleton-based action recognition. However, there is a significant amount of noise in skeleton data, which is simply overlooked by most existing methods. Some methods have designed specialized mechanisms to handle noise, but these mechanisms are either based on prior knowledge or require additional supervision information. To overcome these problems, we propose in this article a fully implicit solution, which embeds a soft-thresholding-based denoising module into existing networks, which can automatically learn to remove noise without any prior knowledge or additional supervision information. In addition, by relaxing the nonnegative constraint, the module gains the ability to adaptively enhance key features. Based on this, we further propose a two-staged method for coupled noise suppression and feature enhancement. The proposed method achieves state-of-the-art performance on public datasets. Moreover, on noise polluted datasets, the proposed method demonstrates significant performance advantages over existing methods.

Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec remains ambiguous due to the unique characteristics of Large Language Models (LLMs).

As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes becomes an imperative requirement. Prior approaches for enhancing fairness in recommender systems presume the availability of all sensitive attributes, which can be difficult to obtain due to privacy concerns or inadequate means of capturing these attributes. In practice, the efficacy of these approaches is limited, pushing us to investigate ways of promoting fairness with limited sensitive attribute information. Toward this goal, it is important to reconstruct missing sensitive attributes. Nevertheless, reconstruction errors are inevitable due to the complexity of real-world sensitive attribute reconstruction problems and legal regulations. Thus, we pursue fair learning methods that are robust to reconstruction errors. To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors. We provide theoretical and empirical evidence to demonstrate that our method can effectively ensure fairness in recommender systems when only limited sensitive attributes are accessible.

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.