In this paper, we propose a novel element-wise subset selection method for the alternating least squares (ALS) algorithm, focusing on low-rank matrix factorization involving matrices with missing values, as commonly encountered in recommender systems. While ALS is widely used for providing personalized recommendations based on user-item interaction data, its high computational cost, stemming from repeated regression operations, poses significant challenges for large-scale datasets. To enhance the efficiency of ALS, we propose a core-elements subsampling method that selects a representative subset of data and leverages sparse matrix operations to approximate ALS estimations efficiently. We establish theoretical guarantees for the approximation and convergence of the proposed approach, showing that it achieves similar accuracy with significantly reduced computational time compared to full-data ALS. Extensive simulations and real-world applications demonstrate the effectiveness of our method in various scenarios, emphasizing its potential in large-scale recommendation systems.

Conversational agents deployed in industrial-scale official account platforms must generate responses that are both contextually grounded and stylistically aligned-requirements that existing methods struggle to meet. Chain-of-thought (CoT) prompting induces significant latency due to multi-turn reasoning; per-account fine-tuning is computationally prohibitive; and long prompt-based methods degrade the model's ability to grasp injected context and style. In this paper, we propose WeStar, a lite-adaptive framework for stylized contextual question answering that scales to millions of official accounts. WeStar combines context-grounded generation via RAG with style-aware generation using Parametric RAG (PRAG), where LoRA modules are dynamically activated per style cluster. Our contributions are fourfold: (1) We introduce WeStar, a unified framework capable of serving large volumes of official accounts with minimal overhead. (2) We propose a multi-dimensional, cluster-based parameter sharing scheme that enables compact style representation while preserving stylistic diversity. (3) We develop a style-enhanced Direct Preference Optimization (SeDPO) method to optimize each style cluster's parameters for improved generation quality. (4) Experiments on a large-scale industrial dataset validate the effectiveness and efficiency of WeStar, underscoring its pracitical value in real-world deployment.

Personalized content marketing has become a crucial strategy for digital platforms, aiming to deliver tailored advertisements and recommendations that match user preferences. Traditional recommendation systems often suffer from two limitations: (1) reliance on limited supervised signals derived from explicit user feedback, and (2) vulnerability to noisy or unintentional interactions. To address these challenges, we propose SeqUDA-Rec, a novel deep learning framework that integrates user behavior sequences with global unsupervised data augmentation to enhance recommendation accuracy and robustness. Our approach first constructs a Global User-Item Interaction Graph (GUIG) from all user behavior sequences, capturing both local and global item associations. Then, a graph contrastive learning module is applied to generate robust embeddings, while a sequential Transformer-based encoder models users' evolving preferences. To further enhance diversity and counteract sparse supervised labels, we employ a GAN-based augmentation strategy, generating plausible interaction patterns and supplementing training data. Extensive experiments on two real-world marketing datasets (Amazon Ads and TikTok Ad Clicks) demonstrate that SeqUDA-Rec significantly outperforms state-of-the-art baselines such as SASRec, BERT4Rec, and GCL4SR. Our model achieves a 6.7% improvement in NDCG@10 and 11.3% improvement in HR@10, proving its effectiveness in personalized advertising and intelligent content recommendation.

This research examines how Artificial Intelligence (AI) can improve participatory budgeting processes within smart cities. In response to challenges like declining civic participation and resource allocation conflicts, the study explores how online political participation can be improved by AI. It investigates the state capacity governments need to implement AI-enhanced participatory tools, considering technological dependencies and vulnerabilities. It analyzes technological and administrative structures, actors, interests, and strategies to understand the dynamics of online political participation technologies in the case of Sao Paulo, Brazil. The study contributes to understanding how technological advancements can reshape participatory budgeting processes. In a broader sense, the research highlights how AI can transform participatory institutions by offering new tools for citizens and also for government officials in charge of participatory processes within smart cities.

As online news consumption grows, personalized recommendation systems have become integral to digital journalism. However, these systems risk reinforcing filter bubbles and political polarization by failing to incorporate diverse perspectives. Stance detection -- identifying a text's position on a target -- can help mitigate this by enabling viewpoint-aware recommendations and data-driven analyses of media bias. Yet, existing stance detection research remains largely limited to short texts and high-resource languages. To address these gaps, we introduce \textsc{K-News-Stance}, the first Korean dataset for article-level stance detection, comprising 2,000 news articles with article-level and 21,650 segment-level stance annotations across 47 societal issues. We also propose \textsc{JoA-ICL}, a \textbf{Jo}urnalism-guided \textbf{A}gentic \textbf{I}n-\textbf{C}ontext \textbf{L}earning framework that employs a language model agent to predict the stances of key structural segments (e.g., leads, quotations), which are then aggregated to infer the overall article stance. Experiments showed that \textsc{JoA-ICL} outperforms existing stance detection methods, highlighting the benefits of segment-level agency in capturing the overall position of long-form news articles. Two case studies further demonstrate its broader utility in promoting viewpoint diversity in news recommendations and uncovering patterns of media bias.

Human-computer interaction has long imagined technology that understands us-from our preferences and habits, to the timing and purpose of our everyday actions. Yet current user models remain fragmented, narrowly tailored to specific apps, and incapable of the flexible reasoning required to fulfill these visions. This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer. The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences. GUMs can infer that a user is preparing for a wedding they're attending from messages with a friend. Or recognize that a user is struggling with a collaborator's feedback on a draft by observing multiple stalled edits and a switch to reading related work. GUMs introduce an architecture that infers new propositions about a user from multimodal observations, retrieves related propositions for context, and continuously revises existing propositions. To illustrate the breadth of applications that GUMs enable, we demonstrate how they augment chat-based assistants with context, manage OS notifications to selectively surface important information, and enable interactive agents that adapt to preferences across apps. We also instantiate proactive assistants (GUMBOs) that discover and execute useful suggestions on a user's behalf using their GUM. In our evaluations, we find that GUMs make calibrated and accurate inferences about users, and that assistants built on GUMs proactively identify and perform actions that users wouldn't think to request explicitly. Altogether, GUMs introduce methods that leverage multimodal models to understand unstructured context, enabling long-standing visions of HCI and entirely new interactive systems that anticipate user needs.

This paper proposes a generative method to dynamically simulate users' short video watching journey for watch time prediction in short video recommendation. Unlike existing methods that rely on multimodal features for video content understanding, our method simulates users' sustained interest in watching short videos by learning collaborative information, using interest changes from existing positive and negative feedback videos and user interaction behaviors to implicitly model users' video watching journey. By segmenting videos based on duration and adopting a Transformer-like architecture, our method can capture sequential dependencies between segments while mitigating duration bias. Extensive experiments on industrial-scale and public datasets demonstrate that our method achieves state-of-the-art performance on watch time prediction tasks. The method has been deployed on Kuaishou Lite, achieving a significant improvement of +0.13\% in APP duration, and reaching an XAUC of 83\% for single video watch time prediction on industrial-scale streaming training sets, far exceeding other methods. The proposed method provides a scalable and effective solution for video recommendation through segment-level modeling and user engagement feedback.

Semantic identifiers (IDs) have proven effective in adapting large language models for generative recommendation and retrieval. However, existing methods often suffer from semantic ID conflicts, where semantically similar documents (or items) are assigned identical IDs. A common strategy to avoid conflicts is to append a non-semantic token to distinguish them, which introduces randomness and expands the search space, therefore hurting performance. In this paper, we propose purely semantic indexing to generate unique, semantic-preserving IDs without appending non-semantic tokens. We enable unique ID assignment by relaxing the strict nearest-centroid selection and introduce two model-agnostic algorithms: exhaustive candidate matching (ECM) and recursive residual searching (RRS). Extensive experiments on sequential recommendation, product search, and document retrieval tasks demonstrate that our methods improve both overall and cold-start performance, highlighting the effectiveness of ensuring ID uniqueness.

Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases -- but translating academic theory into practice is inherently challenging. RS practitioners must balance the competing interests of diverse stakeholders, including providers and users, and operate in dynamic environments. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with other (legal, data, and fairness) teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, particularly when navigating multi-stakeholder and dynamic fairness considerations. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including HCI researchers and practitioners.

Dating apps, booze and clubbing - Jane Austen's Emma comes into the 21st Century And your pushy best friend is trying to sort out your love life. It's Jane Austen's Emma, but not as you know it. For the uninitiated, the 1815 novel follows the charmed life of our protagonist in Regency England as she busies herself interfering in her friends' relationships (or matchmaking, depending on your point of view). In Ava Pickett's fresh adaptation, being staged at London's Rose Theatre, Emma Woodhouse still has all the trademark traits of our beloved original heroine - she's clever, quick-witted, meddling, haughty and occasionally cruel. But instead of navigating society balls and dowries, Pickett's modern Emma is poking her nose into her friends' online dating profiles, having returned home after failing her exams at Oxford University.