Online platforms’ openness makes Recommender Systems (RSs) susceptible to data poisoning attacks, where malicious user profiles are injected into the training dataset to distort recommendation outcomes. However, existing poisoning attack methods often struggle to achieve an optimal effectiveness on both imperceptibility and aggressiveness. To address this issue, we propose a novel poisoning attack method for RSs, named MDPAttack, which consists of three key modules, each focusing on imperceptibility and aggressiveness. Specifically, we first train a Multinomial Diffusion Model (MDM) to model discrete rating data, effectively minimizing information loss during data processing and thereby enhancing the imperceptibility of the generated profiles. Then, we combine the influence function with the Fast Gradient Sign Method (FGSM) to iteratively improve the aggressiveness of poisoning profiles by leveraging template profiles. Finally, these two properties are seamlessly integrated within the MDPAttack framework. Extensive experiments on both classic and modern deep learning-based RSs demonstrate that MDPAttack generates highly imperceptible profiles while maintaining attack performance comparable to state-of-the-art methods.

With the rapid expansion of large language model (LLM) applications, there is an emerging shift in the role of LLM-based AI chatbots from serving merely as general inquiry tools to acting as professional service agents. However, current studies often overlook a critical aspect of professional service agents: the act of communicating in a manner consistent with their professional identities. This is of particular importance in the healthcare sector, where effective communication with patients is essential for achieving professional goals, such as promoting patient well-being by encouraging healthy behaviors. To bridge this gap, we propose LAPI (LLM-based Agent with a Professional Identity), a novel framework for designing professional service agent tailored for medical question-and-answer (Q&A) services, ensuring alignment with a specific professional identity. Our method includes a theory-guided task planning process that decomposes complex professional tasks into manageable subtasks aligned with professional objectives and a pragmatic entropy method designed to generate professional and ethical responses with low uncertainty. Experiments on various LLMs show that the proposed approach outperforms baseline methods, including few-shot prompting, chain-of-thought prompting, across key metrics such as fluency, naturalness, empathy, patient-centricity, and ROUGE-L scores. Additionally, the ablation study underscores the contribution of each component to the overall effectiveness of the approach.

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://github.com/jingrk/HDBN .

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.

Deep learning (DL) models have significantly improved the performance of text classification and text regression tasks. However, DL models are often strikingly vulnerable to adversarial attacks. Many researchers have aimed to develop adversarial attacks against DL models in realistic black-box settings (i.e., assuming no model knowledge is accessible to attackers). These attacks typically operate with a two-phase framework: (1) sensitivity estimation through gradient-based or deletion-based methods to evaluate the sensitivity of each token to the prediction of the target model, and (2) perturbation execution to craft adversarial examples based on the estimated token sensitivity. However, gradient-based and deletion-based methods used to estimate sensitivity often face issues of capturing token directionality and overlapping token sensitivities, respectively. In this study, we propose a novel eXplanation-based method for Adversarial Text Attacks (XATA) that leverages additive feature attribution explainable methods, namely LIME or SHAP, to measure the sensitivity of input tokens when crafting black-box adversarial attacks on DL models performing text classification or text regression. We evaluated XATA's attack performance on DL models executing text classification on the IMDB Movie Review, Yelp Reviews-Polarity, and Amazon Reviews-Polarity datasets and DL models conducting text regression on the My Personality, Drug Review, and CommonLit Readability datasets. The proposed XATA outperformed the existing gradient-based and deletion-based adversarial attack baselines in both tasks. These findings indicate that the ever-growing research focused on improving the explainability of DL models with additive feature attribution explainable methods can provide attackers with weapons to launch targeted adversarial attacks.

Phishing website attack, as one of the most persistent forms of cyber threats, evolves and remains a major cyber threat. Various detection methods (e.g., lookup systems, fraud cue-based methods) have been proposed to identify phishing websites. The limitations of lookup systems (e.g., failing to address newly created attacks) and the fraud cue-based methods (e.g., relying on feature engineering) motivated the development of deep representation-based methods capable of learning deep fraud cues for enhanced anti-phishing capacity. Focusing mostly on URLs, these methods fail to analyze other two important modalities of website content: textual information and visual design. Moreover, the interpretability of these deep learning based methods is limited, reducing model trustworthiness and preventing relevant and actionable intelligence. We propose a multi-modal hierarchical attention model (MMHAM) which jointly learns the deep fraud cues from the three major modalities of website content for phishing website detection. Specifically, MMHAM features an innovative shared dictionary learning approach for aligning representations from different modalities in the attention mechanism. In evaluation experiments, the proposed MMHAM not only learned improved deep cues for enhanced phishing detection, but provided a hierarchical interpretability system from which we could develop phishing threat intelligence to inform phishing websites detection at different levels.