arXiv subjects

Fei Sun

Publications and source records attributed to Fei Sun.

At least 73 records · Page 4Linked to original sources

arXiv2025

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters ``dirty'' data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks, and regularization, purification, self-supervised learning for defending against malicious attacks. Additionally, we summarize evaluation metrics and commonly used datasets for assessing robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to provide readers with a comprehensive understanding of robust recommender systems and to identify key pathways for future research and development. To facilitate ongoing exploration, we maintain a continuously updated GitHub repository with related research: https://github.com/Kaike-Zhang/Robust-Recommender-System.

cs.IR↗

arXiv2025

High-precise determination of critical exponents in holographic QCD

The precise determination of critical exponents is crucial for understanding the properties of strongly interacting matter under extreme conditions. These exponents are fundamentally linked to the system's behavior near the critical end point (CEP), making precise localization of the CEP essential. However, precisely identifying the CEP within the framework of AdS/CFT correspondence presents considerable challenges. In this study, we explore critical phenomena and critical exponents within a holographic QCD model. We achieve high-precision calculations of the CEP's position and dynamically analyze the behavior of the critical exponent as it approaches the CEP using limiting methods. Our results indicate that linear fitting is only appropriate in regions very close to the CEP. Furthermore, we find that although the values of the critical exponents vary when approaching the CEP from different directions, they ultimately converge to a single fixed value, revealing a universal behavior among these exponents. Our research underscores the importance of precisely determining the CEP, selecting the fitting region, and considering the direction of the approach when predicting critical exponents. These findings establish a vital benchmark for identifying critical phenomena and their associated exponents.

hep-ph↗

arXiv2025

LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks

Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of sequential recommender systems renders them vulnerable to poisoning attacks, where fraudulent users are injected into the training data to manipulate learned patterns. Traditional defense strategies predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attack types. To solve the above problems, considering the rich open-world knowledge encapsulated in Large Language Models (LLMs), our research initially focuses on the capabilities of LLMs in the detection of unknown fraudulent activities within recommender systems, a strategy we denote as LLM4Dec. Empirical evaluations demonstrate the substantial capability of LLMs in identifying unknown fraudsters, leveraging their expansive, open-world knowledge. Building upon this, we propose the integration of LLMs into defense strategies to extend their effectiveness beyond the confines of known attacks. We propose LoRec, an advanced framework that employs LLM-Enhanced Calibration to strengthen the robustness of sequential recommender systems against poisoning attacks. LoRec integrates an LLM-enhanced CalibraTor (LCT) that refines the training process of sequential recommender systems with knowledge derived from LLMs, applying a user-wise reweighting to diminish the impact of fraudsters injected by attacks. By incorporating LLMs' open-world knowledge, the LCT effectively converts the limited, specific priors or rules into a more general pattern of fraudsters, offering improved defenses against poisoning attacks. Our comprehensive experiments validate that LoRec, as a general framework, significantly strengthens the robustness of sequential recommender systems.

cs.IR↗

arXiv2025

Reversibly Strain Engineering and Electric-Field Control of Crystal Symmetry in Multiferroic Oxides

Multiferroic oxides, such as BiFeO3, have garnered significant attention due to their coupled ferroelectric, magnetic, and elastic properties, offering exciting opportunities for multifunctional device applications. Controlling phase transitions in these materials is critical for tuning their physical properties and achieving desired functionalities. While numerous studies have focused on ferroelectric-ferroelectric transitions at rhombohedral-tetragonal morphotropic phase boundaries, far less attention has been given to the ferroelectric-antiferroelectric phase boundaries. Such systems hold promise for discovering novel physical phenomena, such as reversible phase transitions, enhanced piezoelectricity, and magnetoelectric coupling. In this work, we report a reversible antiferroelectric-to-ferroelectric phase transition in La doped BiFeO3 thin films. By modulating the residual strain via film thickness, an antiferroelectric orthorhombic phase is stabilized within a ferroelectric rhombohedral phase matrix. Under an external electric field, the phase transitions reversibly between these two states. This discovery not only enriches the understanding of orthorhombic-rhombohedral morphotropic phase boundaries but also provides a potential pathway for developing magnetoelectric devices with enhanced functionality.

cond-mat.mtrl-sci↗

arXiv2025

Personalized Denoising Implicit Feedback for Robust Recommender System

While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noise into this feedback, adversely affecting the accuracy and robustness of recommendations. To address this issue, existing methods typically aim to reduce the training weight of noisy feedback or discard it entirely, based on the observation that noisy interactions often exhibit higher losses in the overall loss distribution. However, we identify two key issues: (1) there is a significant overlap between normal and noisy interactions in the overall loss distribution, and (2) this overlap becomes even more pronounced when transitioning from pointwise loss functions (e.g., BCE loss) to pairwise loss functions (e.g., BPR loss). This overlap leads traditional methods to misclassify noisy interactions as normal, and vice versa. To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user's personal loss distribution. Based on this insight, we propose a resampling strategy to Denoise using the user's Personal Loss distribution, named PLD, which reduces the probability of noisy interactions being optimized. Specifically, during each optimization iteration, we create a candidate item pool for each user and resample the items from this pool based on the user's personal loss distribution, prioritizing normal interactions. Additionally, we conduct a theoretical analysis to validate PLD's effectiveness and suggest ways to further enhance its performance. Extensive experiments conducted on three datasets with varying noise ratios demonstrate PLD's efficacy and robustness.

cs.IR↗

arXiv2024

The 1st Workshop on Human-Centered Recommender Systems

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairness issues, and more. Consequently, this workshop aims to provide a platform for researchers to explore the development of Human-Centered Recommender Systems~(HCRS). HCRS refers to the creation of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. In this workshop, topics will include, but are not limited to, robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. We hope to engage in discussions on how to implement and enhance these properties in recommender systems. Additionally, participants will explore diverse evaluation methods, including innovative metrics that capture user satisfaction and trust. This workshop seeks to foster a collaborative environment for researchers to share insights and advance the field toward more ethical, user-centric, and socially responsible recommender systems.

cs.IR↗

arXiv2024

Fact-Level Confidence Calibration and Self-Correction

Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their outputs. However, current calibration methods for LLMs typically estimate two scalars to represent overall response confidence and correctness, which is inadequate for long-form generation where the response includes multiple atomic facts and may be partially confident and correct. These methods also overlook the relevance of each fact to the query. To address these challenges, we propose a Fact-Level Calibration framework that operates at a finer granularity, calibrating confidence to relevance-weighted correctness at the fact level. Furthermore, comprehensive analysis under the framework inspired the development of Confidence-Guided Fact-level Self-Correction ($\textbf{ConFix}$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones. Extensive experiments across four datasets and six models demonstrate that ConFix effectively mitigates hallucinations without requiring external knowledge sources such as retrieval systems.

cs.CL↗

arXiv2024

Game-theoretic LLM: Agent Workflow for Negotiation Games

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game increases with larger payoff matrices or deeper sequential trees. To address these limitations, we design multiple game-theoretic workflows that guide the reasoning and decision-making processes of LLMs. These workflows aim to enhance the models' ability to compute Nash Equilibria and make rational choices, even under conditions of uncertainty and incomplete information. Experimental results demonstrate that the adoption of these workflows significantly improves the rationality and robustness of LLMs in game-theoretic tasks. Specifically, with the workflow, LLMs exhibit marked improvements in identifying optimal strategies, achieving near-optimal allocations in negotiation scenarios, and reducing susceptibility to exploitation during negotiations. Furthermore, we explore the meta-strategic considerations of whether it is rational for agents to adopt such workflows, recognizing that the decision to use or forgo the workflow constitutes a game-theoretic issue in itself. Our research contributes to a deeper understanding of LLMs' decision-making capabilities in strategic contexts and provides insights into enhancing their rationality through structured workflows. The findings have implications for the development of more robust and strategically sound AI agents capable of navigating complex interactive environments. Code and data supporting this study are available at \url{https://github.com/Wenyueh/game_theory}.

cs.AI↗

arXiv2024

Ultra-broadband acoustic absorber based on periodic acoustic rigid-metaporous composite array

To address the increasingly serious issue of noise pollution, we propose an ultra-broadband and wide-angle acoustic absorber based on a periodic acoustic rigid-metaporous composite array. Numerical simulation results verify the broadband good acoustic absorption performance of the proposed absorber, which can achieve an average absorption coefficient of approximately 90.9% within the frequency band from 500 to 4000 Hz with incident angles ranging from -75 to +75 degrees, thus compensating for the shortcomings of traditional acoustic absorbers that are not as effective at low frequencies. This work will provide a new approach for ultra-broadband and wide-angle acoustic wave absorption and noise suppression.

cs.CE↗

arXiv2024

Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation

Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numerous studies have empirically shown that ACF can also improve recommendation performance compared to traditional CF. Despite these empirical successes, the theoretical understanding of ACF's effectiveness in terms of both performance and robustness remains unclear. To bridge this gap, in this paper, we first theoretically show that ACF can achieve a lower recommendation error compared to traditional CF with the same training epochs in both clean and poisoned data contexts. Furthermore, by establishing bounds for reductions in recommendation error during ACF's optimization process, we find that applying personalized magnitudes of perturbation for different users based on their embedding scales can further improve ACF's effectiveness. Building on these theoretical understandings, we propose Personalized Magnitude Adversarial Collaborative Filtering (PamaCF). Extensive experiments demonstrate that PamaCF effectively defends against various types of poisoning attacks while significantly enhancing recommendation performance.

cs.IR↗

arXiv2024

Pruning Foundation Models for High Accuracy without Retraining

Despite the superior performance, it is challenging to deploy foundation models or large language models (LLMs) due to their massive parameters and computations. While pruning is a promising technique to reduce model size and accelerate the inference, the traditional pruning techniques can hardly be applied for LLMs as they need to finetune the model on the full dataset with multiple epochs consuming massive data and hardware resources. To deal with this problem, post-training pruning methods are proposed to prune LLMs in one-shot without retraining. However, their accuracy after pruning may suffer from certain performance degradation due to the lack of retraining with massive data. To address this issue, in this paper, we first formulate the post-training problem for layer-wise LLM compression to simultaneously prune multiple weights in LLMs. Next, we provide an optimal solution for this problem and design our post-training pruning algorithm for both unstructured and semi-structured sparsity. Our extensive experiments demonstrate the superior performance of the proposed methods in comparison to SOTA baselines across various LLM families including transformer-based LLMs and Mamba-based LLMs. Code link: https://github.com/piuzha/APT

cs.LG↗

arXiv2024

MITA: Bridging the Gap between Model and Data for Test-time Adaptation

Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at batch level, often exhibit suboptimal performance in complex real-world scenarios, particularly when confronting outliers or mixed distributions. This phenomenon stems from a pronounced over-reliance on statistical patterns over the distinct characteristics of individual instances, resulting in a divergence between the distribution captured by the model and data characteristics. To address this challenge, we propose Meet-In-The-Middle based Test-Time Adaptation ($\textbf{MITA}$), which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions, thereby meeting in the middle. MITA pioneers a significant departure from traditional approaches that focus solely on aligning the model to the data, facilitating a more effective bridging of the gap between model's distribution and data characteristics. Comprehensive experiments with MITA across three distinct scenarios (Outlier, Mixture, and Pure) demonstrate its superior performance over SOTA methods, highlighting its potential to significantly enhance generalizability in practical applications.

cs.LG↗

arXiv2024

Understanding the Collapse of LLMs in Model Editing

Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse. Among them, ROME is particularly concerning, as it could disrupt LLMs with only a single edit. In this paper, we study the root causes of such collapse. Through extensive analysis, we identify two primary factors that contribute to the collapse: i) inconsistent handling of prefixed and unprefixed keys in the parameter update equation may result in very small denominators, causing excessively large parameter updates; ii) the subject of collapse cases is usually the first token, whose unprefixed key distribution significantly differs from the prefixed key distribution in autoregressive transformers, causing the aforementioned issue to materialize. To validate our findings, we propose a simple yet effective approach: uniformly using prefixed keys during editing phase and adding prefixes during testing phase to ensure the consistency between training and testing. The experimental results show that the proposed solution can prevent model collapse while maintaining the effectiveness of the edits.

cs.CL↗

arXiv2024

When to Trust LLMs: Aligning Confidence with Response Quality

Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlights the importance of discerning when to trust LLMs, especially in safety-critical domains. Existing methods often express reliability by confidence level, however, their effectiveness is limited by the lack of objective guidance. To address this, we propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD), which leverages reinforcement learning guided by a tailored dual-component reward function. This function integrates quality reward and order-preserving alignment reward functions. Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality. Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy, without causing over-cautious. Furthermore, the aligned confidence provided by CONQORD informs when to trust LLMs, and acts as a determinant for initiating the retrieval process of external knowledge. Aligning confidence with response quality ensures more transparent and reliable responses, providing better trustworthiness.

cs.CL↗

arXiv2024

Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System

Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowing attackers to insert fake users into the system's training data to skew the exposure of certain items, known as poisoning attacks. Adversarial training has emerged as a notable defense mechanism against such poisoning attacks within recommender systems. Existing adversarial training methods apply perturbations of the same magnitude across all users to enhance system robustness against attacks. Yet, in reality, we find that attacks often affect only a subset of users who are vulnerable. These perturbations of indiscriminate magnitude make it difficult to balance effective protection for vulnerable users without degrading recommendation quality for those who are not affected. To address this issue, our research delves into understanding user vulnerability. Considering that poisoning attacks pollute the training data, we note that the higher degree to which a recommender system fits users' training data correlates with an increased likelihood of users incorporating attack information, indicating their vulnerability. Leveraging these insights, we introduce the Vulnerability-aware Adversarial Training (VAT), designed to defend against poisoning attacks in recommender systems. VAT employs a novel vulnerability-aware function to estimate users' vulnerability based on the degree to which the system fits them. Guided by this estimation, VAT applies perturbations of adaptive magnitude to each user, not only reducing the success ratio of attacks but also preserving, and potentially enhancing, the quality of recommendations. Comprehensive experiments confirm VAT's superior defensive capabilities across different recommendation models and against various types of attacks.

cs.IR↗

arXiv2024

Creation of independently controllable and long lifetime polar skyrmion textures in ferroelectric-metallic heterostructures

Topological textures like vortices, labyrinths and skyrmions formed in ferroic materials have attracted extensive interests during the past decade for their fundamental physics, intriguing topology, and technological prospects. So far, polar skyrmions remain scarce in ferroelectrics as they require a delicate balance between various dipolar interactions. Here, we report that PbTiO3 thin films in a metallic contact undergo a topological phase transition and stabilize a broad family of skyrmion-like textures (e.g., skyrmion bubbles, multiple π-twist target skyrmions, and skyrmion bags) with independent controllability, analogous to those reported in magnetic systems. Weakly-interacted skyrmion arrays with a density over 300 Gb/inch2 are successfully written, erased and read-out by local electrical and mechanical stimuli of a scanning probe. Interestingly, in contrast to the relatively short lifetime <20 hours of the skyrmion bubbles, the multiple π-twist target skyrmions and skyrmion bags show topology-enhanced stability with lifetime over two weeks. Experimental and theoretical analysis implies the heterostructures carry electric Dzyaloshinskii-Moriya interaction mediated by oxygen octahedral tiltings. Our results demonstrate ferroelectric-metallic heterostructures as fertile playground for topological states and emergent phenomena.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems

Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items. Current attack methods involve iteratively retraining a surrogate recommender on the poisoned data with the latest fake users to optimize the attack. However, this repetitive retraining is highly time-consuming, hindering the efficient assessment and optimization of fake users. To mitigate this computational bottleneck and develop a more effective attack in an affordable time, we analyze the retraining process and find that a change in the representation of one user/item will cause a cascading effect through the user-item interaction graph. Under theoretical guidance, we introduce \emph{Gradient Passing} (GP), a novel technique that explicitly passes gradients between interacted user-item pairs during backpropagation, thereby approximating the cascading effect and accelerating retraining. With just a single update, GP can achieve effects comparable to multiple original training iterations. Under the same number of retraining epochs, GP enables a closer approximation of the surrogate recommender to the victim. This more accurate approximation provides better guidance for optimizing fake users, ultimately leading to enhanced data poisoning attacks. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our proposed GP.

cs.IR↗

arXiv2024

Two-dimensional superconductivity in a thick exfoliated kagome film

We report the observation of two-dimensional superconductivity (2D SC) in exfoliated kagome metal CsV$_3$Sb$_5$ with a thickness far thicker than the atomic limit. By examining the critical current and upper critical magnetic fields ($H_{c2}$) of 40-60 nm thick films in the superconducting state, we identify a pronounced Berezinskii-Kosterlitz-Thouless (BKT) transition behavior, i.e. a drastic decrease of the superfluid stiffness near the transition, and a cusp-like feature of the angular dependent $H_{c2}$, both of which serve as direct evidence of 2D SC. In addition, an exceeding of the Pauli paramagnetic limit of the in-plane $H_{c2}$ is consistent with the 2D SC nature. The observed 2D SC occurs in thick films with the highest superconducting transition temperature $T_c$ and the lowest charge density wave transition temperature $T_{\rm {CDW}}$, which suggests that the charge density wave states are interrelated with the superconducting states. Our findings impose constraints in understanding the enhancement of SC in kagome superconductors, and illuminate pathways for achieving novel 2D superconducting states in more stable and much thicker systems.

cond-mat.supr-con↗