Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel task, termed incomplete multi-view multi-label class incremental learning (IMvMLCIL), which requires models to simultaneously address heterogeneous missing views and dynamic class expansion. To tackle this task, we propose E2PL, an Effective and Efficient Prompt Learning framework for IMvMLCIL. E2PL unifies two novel prompt designs: task-tailored prompts for class-incremental adaptation and missing-aware prompts for the flexible integration of arbitrary view-missing scenarios. To fundamentally address the exponential parameter explosion inherent in missing-aware prompts, we devise an efficient prototype tensorization module, which leverages atomic tensor decomposition to elegantly reduce the prompt parameter complexity from exponential to linear w.r.t. the number of views. We further incorporate a dynamic contrastive learning strategy explicitly model the complex dependencies among diverse missing-view patterns, thus enhancing the model's robustness. Extensive experiments on three benchmarks demonstrate that E2PL consistently outperforms state-of-the-art methods in both effectiveness and efficiency. The codes and datasets are available at https://anonymous.4open.science/r/code-for-E2PL.

Reinforcement learning-based recommendation systems (RLRS) are increasingly favored for their ability to leverage online interactive feedback, enabling adaptive and personalized decision-making. In this setting, user feedback serves as both a behavioral signal and an optimization target, making it essential for policy learning. However, collecting such feedback, e.g., clicks, ratings, and engagement traces, raises serious privacy concerns, posing critical challenges for value estimation, online adaptation, and privacy protection. In this paper, we propose FeedGuard, a critic-guided reinforcement learning framework with privacy-preserving feedback. FeedGuard enhances trajectory modeling via critic guidance, enables joint online fine-tuning with effective exploration–exploitation tradeoffs, and enforces end-to-end privacy protection across the feedback lifecycle via split federated learning and differential privacy. We further provide a formal analysis of its differential privacy guarantees. Extensive experiments on four public recommendation datasets and the VirtualTB platform show that FeedGuard performs well in both offline and online settings, while maintaining rigorous privacy guarantees with minimal degradation.

Hash-based collaborative filtering (Hash-CF) approaches recently employ efficient Hamming distance of learned binary representations to accelerate recommendations. Benefiting from its probabilistic nature, Variational Autoencoder (VAE) enables robust Hash-CF with stronger generalization ability. However, VAE-based Hash-CF still faces two challenging problems: 1) Traditional VAE urges the latent variables of different users (or items) to fit a unified and monotonous prior distribution, and lacks considerations for distinctive characteristics of users (or items). The obtained representations of users and items with slight individual differentiation may further weaken the performance of Hash-CF for subsequent personalized recommendations. 2) Hash-CF under the VAE framework requires discrete optimization on latent Bernoulli distributions, which are discrete and NP-hard to optimize. In this paper, we propose a Dual Discrete Collaborative Filtering (DDCF) approach, including a cluster-enhanced representation generation module and a CNF-enabled discrete optimization module. The former module mainly develops cluster-aware latent space to generate discriminative representations for users or items with significantly different characteristics. The latter module employs Continuous Normalizing Flow (CNF) to achieve discrete optimization on latent Bernoulli distributions steadily and effectively. Extensive experiments conducted on multiple real-world datasets demonstrate the superiority of our DDCF compared with the state-of-art methods in terms of effectiveness and efficiency.

Data cleaning (DC) is a crucial yet challenging step for many data engineering tasks. Traditional pre-configuration DC methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free DC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose azero-training and interpretable DCsystem, named${\sf ZeroDC}$, that leverageslarge language models(LLMs) to generate data cleaning rules and chain-of-thoughts (CoTs), without the need for model training.${\sf ZeroDC}$consists of two modules,iterative detection rule generation(IDG) andtraining-free explainable correction(TEC). To generate high-quality error detection rules with minimal human feedback, IDG first bootstraps a set of rules viacontrastive rule initiationon sampled syntactic and semantic contrastive pairs. It then progressively enhances them through aniterative rule refinementworkflow that selects the most informative elements for updates. TEC constructs acontextual-relevant tuple retrieverusing aweighted cosine similarityfunction to efficiently identify the most relevant tuples for each dirty value, reducing redundancy in the LLM prompts and lowering computational costs. It further prompts for generatingcorrection CoTsfor user-corrected representative values, as well as prompts for creatingcorrection rulesandexplainable corrections, which automatically provide explanations for correction results, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that${\sf ZeroDC}$achieves, on average, a 5.36% increase in accuracy and an 8.16x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available athttps://github.com/YangChen32768/ZeroDC.

Correcting missing or erroneous data values is an essential task in data cleaning. Traditional pre-configuration error correction (EC) methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free EC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose a zero-training and interpretable EC system, named ZeroEC, that leverages large language models (LLMs) to generate chain-of-thoughts (CoTs) and correction rules for EC, without the need for model training. ZeroEC consists of two modules, contextual-relevant tuple search (CTS) and training-free explainable correction (TEC). CTS constructs a contextual-relevant tuple retriever using a weighted cosine similarity function to efficiently identify the most relevant tuples for each dirty tuple, reducing redundancy in the LLM prompts and lowering computational costs. TEC employs a clustering-based representative tuple sampling strategy to alleviate “hallucination” risk by exposing LLMs to diverse types of data errors. It further prompts for generating correction CoTs for user-corrected representative tuples, as well as prompts for creating correction rules and explainable ECs, which automatically provide explanations for EC, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that ZeroEC achieves a 66.82% increase in accuracy and a 6.87x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available at https://github.com/YangChen32768/ZeroEC.

Incomplete multi-view partial multi-label classification (IMvPMLC), which tackles the combined challenges of incompleteness in both multi-view and multi-label problems, has drawn considerable attention. Existing IMvPMLC methods have made progress but still face several challenges: (i) They mainly focus on the consistency of representations across multiple views but overlook the relationships among instances, leading to suboptimal representations. (ii) They primarily utilize only the available labels for supervised learning, ignoring the missing label distribution and limiting their ability to capture label correlations. In this paper, we propose a novel model named Dual Structure-guided Contrastive Network (DSCN) for IMvPMLC. Specifically, we introduce a similarity-guided instance-level contrastive learning mechanism to achieve multi-view consistent and discriminative representations across instances by leveraging instance structures, while a multi-view attention-based fusion strategy dynamically facilitates the fusion of multi-view representations to derive a robust consensus representation. Then, we design a multi-view shared classifier integrated with a correlation-guided label-level contrastive learning mechanism to enhance predictions by leveraging complementary information across multiple views and capturing label structures, effectively exploiting missing label distribution. Extensive experiments on five benchmark datasets demonstrate that, DSCN yields a more than 13% accuracy, compared with the state-of-the-art approaches. The code and datasets are available at https://anonymous.4open.science/r/DSCN-D471.

The sparse portfolio optimization (SPO) problem is increasingly crucial in portfolio management, focusing on selecting a few stocks with the potential for strong market performance. However, sparse portfolio strategies often face significant short-term drawdowns during periods of market volatility. To this end, a news-driven portfolio strategy offers valuable insights to capture sudden market changes. Nevertheless, it encounters two main challenges:how to reasonably map the relationships between news and stocksandhow to effectively utilize the irregular timing of news releases. To tackle the SPO problem in fluctuating markets while addressing these challenges, we propose a novel news-driven sparse portfolio strategy, named SPIN. Specifically, SPIN not only leverages industry-specific group structures existing among stocks for a more reasonable news-stock mapping and models news sequential patterns based on our devised novel news-driven forecaster to handle the irregularity of news releases. We rigorously prove that SPIN achieves a sub-linear regret. Extensive experiments on three real-world datasets demonstrate SPIN's superiority over state-of-the-art portfolio strategies in terms of cumulative wealth and short-term drawdowns.

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly foucsed on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.