General Data Protection Regulations (GDPR) aim to safeguard individuals' personal information from harm. While full compliance is mandatory in the European Union and the California Privacy Rights Act (CPRA), it is not in other places. GDPR requires simultaneous compliance with all the principles such as fairness, accuracy, and data minimization. However, it overlooks the potential contradictions within its principles. This matter gets even more complex when compliance is required from decision-making systems. Therefore, it is essential to investigate the feasibility of simultaneously achieving the goals of GDPR and machine learning, and the potential tradeoffs that might be forced upon us. This paper studies the relationship between the principles of data minimization and fairness in recommender systems. We operationalize data minimization via active learning (AL) because, unlike many other methods, it can preserve a high accuracy while allowing for strategic data collection, hence minimizing the amount of data collection. We have implemented several active learning strategies (personalized and non-personalized) and conducted a comparative analysis focusing on accuracy and fairness on two publicly available datasets. The results demonstrate that different AL strategies may have different impacts on the accuracy of recommender systems with nearly all strategies negatively impacting fairness. There has been no to very limited work on the trade-off between data minimization and fairness, the pros and cons of active learning methods as tools for implementing data minimization, and the potential impacts of AL on fairness. By exploring these critical aspects, we offer valuable insights for developing recommender systems that are GDPR compliant.

Advances in generative AI point towards a new era of personalized applications that perform diverse tasks on behalf of users. While general AI assistants have yet to fully emerge, their potential to share personal data raises significant privacy challenges. This paper introduces CI-Bench, a comprehensive synthetic benchmark for evaluating the ability of AI assistants to protect personal information during model inference. Leveraging the Contextual Integrity framework, our benchmark enables systematic assessment of information flow across important context dimensions, including roles, information types, and transmission principles. We present a novel, scalable, multi-step synthetic data pipeline for generating natural communications, including dialogues and emails. Unlike previous work with smaller, narrowly focused evaluations, we present a novel, scalable, multi-step data pipeline that synthetically generates natural communications, including dialogues and emails, which we use to generate 44 thousand test samples across eight domains. Additionally, we formulate and evaluate a naive AI assistant to demonstrate the need for further study and careful training towards personal assistant tasks. We envision CI-Bench as a valuable tool for guiding future language model development, deployment, system design, and dataset construction, ultimately contributing to the development of AI assistants that align with users' privacy expectations.

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary statistics and distributional similarities. However, the complexity of human mobility patterns is oversimplified by these similarities (a.k.a. ``Datasaurus''), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. It imitates the human decision-making process in trajectory generation, rather than fitting any specific statistical distributions as traditional methods do, thus avoiding the Datasaurus issue. Moreover, we also propose a comprehensive task-based evaluation protocol beyond Datasaurus to systematically benchmark trajectory generative models on four typical downstream tasks, integrating multiple techniques and evaluation metrics for each task, to comprehensively assess the ultimate utility of the generated trajectories. We conduct a thorough evaluation of MIRAGE on three real-world user trajectory datasets against a sizeable collection of baselines. Results show that compared to the best baselines, MIRAGE-generated trajectory data not only achieves the best statistical and distributional similarities with 59.0-71.5% improvement, but also yields the best performance in the task-based evaluation with 10.9-33.4% improvement.

Accurate attribute extraction is critical for beauty product recommendations and building trust with customers. This remains an open problem, as existing solutions are often unreliable and incomplete. We present a system to extract beauty-specific attributes using end-to-end supervised learning based on beauty product ingredients. A key insight to our system is a novel energy-based implicit model architecture. We show that this implicit model architecture offers significant benefits in terms of accuracy, explainability, robustness, and flexibility. Furthermore, our implicit model can be easily fine-tuned to incorporate additional attributes as they become available, making it more useful in real-world applications. We validate our model on a major e-commerce skincare product catalog dataset and demonstrate its effectiveness. Finally, we showcase how ingredient-based attribute extraction contributes to enhancing the explainability of beauty recommendations.

As recommender systems become widely deployed in different domains, they increasingly influence their users' beliefs and preferences. Auditing recommender systems is crucial as it not only ensures the continuous improvement of recommendation algorithms but also safeguards against potential issues like biases and ethical concerns. In this paper, we view recommender system auditing from a causal lens and provide a general recipe for defining auditing metrics. Under this general causal auditing framework, we categorize existing auditing metrics and identify gaps in them -- notably, the lack of metrics for auditing user agency while accounting for the multi-step dynamics of the recommendation process. We leverage our framework and propose two classes of such metrics:future- and past-reacheability and stability, that measure the ability of a user to influence their own and other users' recommendations, respectively. We provide both a gradient-based and a black-box approach for computing these metrics, allowing the auditor to compute them under different levels of access to the recommender system. In our experiments, we demonstrate the efficacy of methods for computing the proposed metrics and inspect the design of recommender systems through these proposed metrics.

Features (a.k.a. context) are critical for contextual multi-armed bandits (MAB) performance. In practice of large scale online system, it is important to select and implement important features for the model: missing important features can led to sub-optimal reward outcome, and including irrelevant features can cause overfitting, poor model interpretability, and implementation cost. However, feature selection methods for conventional machine learning models fail short for contextual MAB use cases, as conventional methods select features correlated with the outcome variable, but not necessarily causing heterogeneuous treatment effect among arms which are truely important for contextual MAB. In this paper, we introduce model-free feature selection methods designed for contexutal MAB problem, based on heterogeneous causal effect contributed by the feature to the reward distribution. Empirical evaluation is conducted based on synthetic data as well as real data from an online experiment for optimizing content cover image in a recommender system. The results show this feature selection method effectively selects the important features that lead to higher contextual MAB reward than unimportant features. Compared with model embedded method, this model-free method has advantage of fast computation speed, ease of implementation, and prune of model mis-specification issues.

The suspect in Charlie Kirk's assassination has been captured, FBI director Kash Patel announced MSNBC sparks outrage for'disgusting' Charlie Kirk comments following Utah shooting Tragedy as Charlie Kirk's wife left behind with two young children after conservative activist is fatally shot A DEI mayor, an inconvenient crime and video they never wanted you to see: MAUREEN CALLAHAN knows why the Left has sympathy for that killer... but none for his victim Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season We only had one symptom we dismissed... but then we were diagnosed with the rarest form of melanoma Soft-touch prosecutor let felon walk free... before crook'slit Auburn professor's throat in random attack' I tried the 30 cent'miracle chill pill' before a big event.. now I'm taking it for everything Donald Trump and House Republicans lead prayers for Charlie Kirk's family after conservative star is fatally shot Prince Harry says his father King Charles is'great' following their first meeting in 19 months... which was over a cup of tea and just 55 minutes long Liberal media defends thug who killed Ukrainian woman in cold blood: 'This man was hurting' Knifeman accused of stabbing Ukrainian refugee to death gives chilling reason for the attack... as he speaks for the first time from jail on the murder that shocked America Fox News reveals new lineup and elevates star White House reporter who's sparred with Trump Horrific new details of passenger injuries after they were'thrown' around Delta flight during'severe turbulence' British rail operator BANS passengers from using dating apps on board - but claims there's a'good reason' for the move For many singletons, the daily commute is the perfect opportunity to get online and browse through your dating apps. But if you travel with Northern, there's bad news for you. The rail operator's on-board WiFi will now block access to all dating apps and websites. Northern says the ban has been driven by the fear that children on board its trains will see inappropriate content. 'Whilst some dating websites - and users - will operate with appropriate levels of self-moderation, some might not and it's important that content not suitable for everyone to see or hear - particularly children - isn't viewed on our trains,' said Matt Rice, chief operating officer at Northern.

Learning user preferences from implicit feedback is one of the core challenges in recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore, various denoising recommendation methods have been proposed recently. However, most of them overly rely on the hyperparameter configurations, inevitably leading to inadequacies in model adaptability and generalization performance. In this study, we propose a novel Adaptive Ensemble Learning (AEL) for denoising recommendation, which employs a sparse gating network as a brain, selecting suitable experts to synthesize appropriate denoising capacities for different data samples. To address the ensemble learning shortcoming of model complexity and ensure sub-recommender diversity, we also proposed a novel method that stacks components to create sub-recommenders instead of directly constructing them. Extensive experiments across various datasets demonstrate that AEL outperforms others in kinds of popular metrics, even in the presence of substantial and dynamic noise. Our code is available at https://github.com/cpu9xx/AEL.

The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previously deemed impractical. However, the GPU-based computational costs present substantial challenges. In this paper, we demonstrate our development of an efficiency-driven approach to explore these paradigms, moving beyond traditional reliance on native PyTorch modules. We address the specific challenges posed by ranking models' dependence on categorical features, which vary in length and complicate GPU utilization. We introduce Jagged Feature Interaction Kernels, a novel method designed to extract fine-grained insights from long categorical features through efficient handling of dynamically sized tensors. We further enhance the performance of attention mechanisms by integrating Jagged tensors with Flash Attention. Our novel Jagged Flash Attention achieves up to 9x speedup and 22x memory reduction compared to dense attention. Notably, it also outperforms dense flash attention, with up to 3x speedup and 53% more memory efficiency. In production models, we observe 10% QPS improvement and 18% memory savings, enabling us to scale our recommendation systems with longer features and more complex architectures.

Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources. Recommending by user-wise result caches is widely used when the system cannot afford a real-time recommendation. However, it is challenging to allocate real-time and cached recommendations to maximize the users' overall engagement. This paper shows two key challenges to cache allocation, i.e., the value-strategy dependency and the streaming allocation. Then, we propose a reinforcement prediction-allocation framework (RPAF) to address these issues. RPAF is a reinforcement-learning-based two-stage framework containing prediction and allocation stages. The prediction stage estimates the values of the cache choices considering the value-strategy dependency, and the allocation stage determines the cache choices for each individual request while satisfying the global budget constraint. We show that the challenge of training RPAF includes globality and the strictness of budget constraints, and a relaxed local allocator (RLA) is proposed to address this issue. Moreover, a PoolRank algorithm is used in the allocation stage to deal with the streaming allocation problem. Experiments show that RPAF significantly improves users' engagement under computational budget constraints.