Model Inversion Attacks (MIAs) pose a significant threat to data privacy by reconstructing sensitive training samples from the knowledge embedded in trained machine learning models. Despite recent progress in enhancing the effectiveness of MIAs across diverse settings, defense strategies have lagged behind—struggling to balance model utility with robustness against increasingly sophisticated attacks. In this work, we propose the ideal inversion error to measure the privacy leakage, and our theoretical and empirical investigations reveals that higher-rank features are inherently more prone to privacy leakage. Motivated by this insight, we propose a lightweight and effective defense strategy based on low-rank feature filtering, which explicitly reduces the attack surface by constraining the dimension of intermediate representations. Extensive experiments across various model architectures and datasets demonstrate that our method consistently outperforms existing defenses, achieving state-of-the-art performance against a wide range of MIAs. Notably, our approach remains effective even in challenging regimes involving high-resolution data and high-capacity models, where prior defenses fail to provide adequate protection. The code is available at https://github.com/Chrisqcwx/LoFt.

Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While frequency-domain analysis offers a global perspective to address this, existing approaches typically treat user sequences in isolation, overlooking the crucial context of the target item. In this work, we present a novel empirical observation: user attention scores exhibit distinct spectral entropy distributions when conditioned on positive versus negative target items. Specifically, true user interests manifest as highly concentrated spectral patterns with lower entropy in the frequency domain, whereas irrelevant behaviors appear as high-entropy noise. Leveraging this insight, we propose the Frequency-Enhanced Deep Interest Network (FEDIN). FEDIN introduces a frequency-domain branch that utilizes a target-aware spectrum filtering mechanism to isolate these periodic interest signals. Extensive experiments on three public datasets demonstrate that FEDIN consistently outperforms state-of-the-art sequential recommendation baselines, demonstrating superior robustness against noise. We have released our code at: https://github.com/otokoneko/FEDIN.

Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains sufficient advantages of federated learning while supporting independent local serving. To achieve this goal, we propose the carefully designed Joint Privileged Learning framework (JPL) to i) alleviate the absence of the passive party's feature with federated equivalence imitation and ii) adapt to the heterogeneous full sample space with cross-branch rank alignment. Extensive experiments conducted on real-world advertising datasets validate the effectiveness of our method over baseline methods.

The web-scale surge of multimodal content, including short-video feeds and autonomous sensing streams, has made web-native lossless compression a prerequisite for delivery and storage across browsers and edge–cloud pipelines. However, existing methods often fail to adapt to shifting distributions across different batches and struggle to balance computational resources in the face of large conditional entropy disparities among diverse modalities. To address these limitations, we propose MoE-LC, a new mixture-of-experts framework for multi-modal lossless compression that dynamically accommodates heterogeneous data distributions and varying complexity levels. First, the Batch-Adaptive Experts (BAE) module introduces batch-specific parameters with a residual gating mechanism, ensuring stable modeling under non-stationary distributions. Second, the Entropy-Aware Multi-Expert Selection (MES) strategy adaptively allocates the number of experts according to the data's estimated compression difficulty (entropy), thereby improving resource utilization and computational efficiency. Finally, the Precision-Aware Expert Routing (PER) component applies high-precision computation solely to the most critical experts, significantly reducing overhead without sacrificing compression accuracy. Experimental results across multiple real-world datasets demonstrate that MoE-LC achieves 5.33%--70.89% improvements in compression ratio and 37.25%--1532.41% gains in throughput compared to advanced baselines, offering a scalable solution for real-time, large-scale multi-modal data compression. Our code is available at https://github.com/Magie0/MoE_LC.

Sequential recommendation (SR) aims to capture dynamic user preferences from users' historical behaviors. Recently, benefiting from astonishing understanding ability of pre-trained language models (PLMs), text-enhanced sequential recommender becomes a promising direction, which employs PLMs to extract semantic information for user/item representation. Although promising in improving performance and transferability, few existing text-enhanced SR studies have analyzed the differences between PLMs and recommenders, restricting the ability of PLMs for recommendation. In this paper, we make an in-depth comparison and conclude their discrepancies in representation and knowledge level, respectively, caused by different multi-modal content and task-oriented capabilities. Based on this, we propose a Recommendation-Oriented Language Model Adaptation framework (named ROMA) using multi-modal multi-domain item sequences. To empower PLMs with a rational understanding of user/item modeling and the recommendation task, ROMA partitions a PLM into bottom and top layers, respectively, allowing representation-level and task-level adaptation with elaborately designed architectures, transferring strategy and learning framework. Our experimental results on public benchmarks demonstrate the effectiveness and transferability of our framework. Additionally, we showcase the application value of ROMA on the recommender system of Huawei's AppGallery through online A/B testing, which shows significant improvements in online metrics.

The dynamic nature of stock market styles, referred to as concept drift, poses a formidable challenge when applying deep learning to stock prediction. Models trained on historical data often struggle to adapt to the latest market styles, as the patterns they have learned may no longer hold true over time. To alleviate this issue, the recently popularized concept of In-Context learning has provided us with valuable insights. In this approach, large language models (LLMs) are exposed to multiple examples of input-label pairs, also known as demonstrations, as part of the prompt before performing a task on an unseen example. By thoroughly analyzing these demonstrations, LLMs can uncover potential patterns and effectively adapt to new tasks. Building upon this concept, we propose a Context-Informed drift-aware method for Stock Prediction (CISP), which continually adjusts to the latest market styles and offers more accurate predictions. Our proposed method consists of two key parts. Firstly, we introduce a straightforward and efficient technique for designing demonstrations that aggregate current market information, thereby indicating the prevailing stock market style. Secondly, we incorporate a prediction module with dynamic parameters, allowing it to appropriately adjust its model parameters based on the market patterns embedded in the aforementioned demonstrations. Through extensive experiments conducted on real-world stock market datasets, our approach consistently outperforms the most advanced existing methods for stock prediction.

Music recommendation is usually modeled as a Click-Through Rate (CTR) prediction problem, which estimates the probability of a user listening a recommended song. CTR prediction can be formulated as a binary classification problem where the played songs are labeled as positive samples and the skipped songs are labeled as negative samples. However, such naively defined labels are noisy and biased in practice, causing inaccurate model predictions. In this work, we first identify serious label noise issues in an industrial music App, and then propose an adaptive Label Correction method for Denoising (LCD) music recommendation by ensembling the noisy labels and the model outputs to encourage a consensus prediction. Extensive offline experiments are conducted to evaluate the effectiveness of LCD on both industrial and public datasets. Furthermore, in a one-week online AB test, LCD also significantly increases both the music play count and time per user by 1% to 5%.

Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and parameters to obtain local training data by launching reconstruction and membership inference attacks. To defend against such privacy attacks, many noises perturbed methods (like differential privacy or CountSketch matrix) have been widely designed. However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee). To overcome this issue, we propose an efficient model perturbation method for federated learning to defend against reconstruction and membership inference attacks launched by curious clients. On the one hand, similar to the differential privacy, our method also selects random numbers as perturbed noises added to the global model parameters, and thus it is very efficient and easy to be integrated in practice. Meanwhile, the random selected noises are positive real numbers and the corresponding value can be arbitrarily large, and thus the strong defence ability can be ensured. On the other hand, unlike differential privacy or other perturbation methods that cannot eliminate added noises, our method allows the server to recover the true aggregated gradients by eliminating the added noises. Therefore, our method does not hinder learning accuracy at all. Extensive experiments demonstrate that for both regression and classification tasks, our method achieves the same accuracy as non-private approaches and outperforms the state-of-the-art defence schemes. Besides, the defence ability of our method against reconstruction and membership inference attack is significantly better than the state-of-the-art related defence schemes.

With the recent boom of video-based social platforms (e.g., YouTube and TikTok), video retrieval using sentence queries has become an important demand and attracts increasing research attention. Despite the decent performance, existing text-video retrieval models in vision and language communities are impractical for large-scale Web search because they adopt brute-force search based on high-dimensional embeddings. To improve efficiency, Web search engines widely apply vector compression libraries (e.g., FAISS [26]) to post-process the learned embeddings. Unfortunately, separate compression from feature encoding degrades the robustness of representations and incurs performance decay. To pursue a better balance between performance and efficiency, we propose the first quantized representation learning method for cross-view video retrieval, namely Hybrid Contrastive Quantization (HCQ). Specifically, HCQ learns both coarse-grained and fine-grained quantizations with transformers, which provide complementary understandings for texts and videos and preserve comprehensive semantic information. By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels. This hybrid-grained learning strategy serves as strong supervision on the cross-view video quantization model, where contrastive learning at different levels can be mutually promoted. Extensive experiments on three Web video benchmark datasets demonstrate that HCQ achieves competitive performance with state-of-the-art non-compressed retrieval methods while showing high efficiency in storage and computation. Code and configurations are available at https://github.com/gimpong/WWW22-HCQ.

The falling accident is one of the largest threats to human health, which leads to broken bones, head injury, or even death. Therefore, automatic human fall recognition is vital for the Activities of Daily Living (ADL). In this paper, we try to define multi-level computer vision tasks for the visually observed fall recognition problem and study the methods and pipeline. We make frame-level labels for the fall action on several ADL datasets to test the methods and support the analysis. While current deep-learning fall recognition methods usually work on the sequence-level input, we propose a novel Dynamic Pose Motion (DPM) representation to go a step further, which can be captured by a flexible motion extraction module. Besides, a sequence-level fall recognition pipeline is proposed, which has an explicit two-branch structure for the appearance and motion feature, and has canonical LSTM to make temporal modeling and fall prediction. Finally, while current research only makes a binary classification on the fall and ADL, we further study how to detect the start time and the end time of a fall action in a video-level task. We conduct analysis experiments and ablation studies on both the simulated and real-life fall datasets. The relabelled datasets and extensive experiments form a new baseline on the recognition of falls and ADL.

This paper studies a class of binary matrices with correlations between distinct columnsequal to zero or one, which has reported comparable performance with random matrices inrecent studies of compressed sensing. For such matrix, we analyze its structure propertyand provide an improved performance estimation.

In this paper, we propose a keyword proximity search approach that can be applied to large and complex RDF database. We model RDF database as undirected data graph, construct three indexes for each data graph, only one index need be loaded into memory. Keyword graph is defined as search result, keyword tree and minimal keyword tree are proposed as middle structures for Keyword graph extraction, and we present a link join operation based algorithm to retrieve Keyword trees in this paper. We employ a technique of keyword node pruning to accelerate keyword tree retrieval and define a scoring function to rank search results. In experiments, our approach achieves both high efficiency and high accuracy, outperforms the existing approaches.