Energy efficiency has become a critical requirement, driving recommendation systems for resource-constrained environments such as edge devices. Model quantization offers an effective way to build low-bitwidth models while preserving accuracy. However, user–item interaction graphs contain numerous nodes and complex topological structures, leading nodes to exhibit unique similarities and differences. Existing quantization methods uniformly process parameters in high-dimensional DNN layers (e.g., linear, convolutional, or attention layers), while inadequately capturing such similarities among node embeddings. This paper proposes GraphQ, a chunk-wise quantization framework for graph collaborative filtering that supports both the training and post-training phases in a unified perspective. Our core idea is to adaptively partition node embeddings into multiple chunks based on the distribution of embedding values, and then apply chunk-wise quantization. Specifically, for quantization-aware training (QAT), we introduce learnable low-precision quantization factors that partition node embeddings into multiple chunks and are dynamically updated following message passing. For post-training quantization (PTQ), we first cluster nodes and then partition their dimensions into chunks for weight clipping. Extensive experiments on four real-world datasets show that GraphQ outperforms state-of-the-art QAT methods by an average of 27.49% in Recall@10 under the 256-dimensional embedding and 2-bit settings, and surpasses PTQ methods by 78.64% on average under 4-bit settings.

Sequential recommendation models, particularly those based on attention, achieve strong accuracy but incur quadratic complexity, making long user histories prohibitively expensive. Sub-quadratic operators such as Hyena provide efficient alternatives in language modeling, but their potential in recommendation remains underexplored. We argue that Hyena faces challenges in recommendation due to limited representation capacity on sparse, long user sequences. To address these challenges, we propose HyenaRec, a novel sequential recommender that integrates polynomial-based kernel parameterization with gated convolutions. Specifically, we design convolutional kernels using Legendre orthogonal polynomials, which provides a smooth and compact basis for modeling long-term temporal dependencies. A complementary gating mechanism captures fine-grained short-term behavioral bursts, yielding a hybrid architecture that balances global temporal evolution with localized user interests under sparse feedback. This construction enhances expressiveness while scaling linearly with sequence length. Extensive experiments on multiple real-world datasets demonstrate that HyenaRec consistently outperforms Attention-, Recurrent-, and other baselines in ranking accuracy. Moreover, it trains significantly faster (up to 6× speedup), with particularly pronounced advantages on long-sequence scenarios where efficiency is maintained without sacrificing accuracy. These results highlight polynomial-based kernel parameterization as a principled and scalable alternative to attention for sequential recommendation.

This is an erratum for the article "A Dual Perspective Framework of Knowledge-correlation for Cross-domain Recommendation" published in ACM Trans. Knowl. Discov. Data 18(6): 152:1-152:28 (2024).

Cross-domain recommendation (CDR) aims to utilize the information from relevant domains to guide the recommendation task in the target domain, and shows great potential in alleviating the data sparsity and cold-start problems of recommender systems. Most existing methods utilize the interaction information (e.g., ratings and clicks) or consider auxiliary information (e.g., tags and comments) to analyze the users' cross-domain preferences, but such kinds of information ignore the intrinsic semantic relationship of different domains. In order to effectively explore the inter-domain correlations, encyclopedic knowledge graphs (KG) involving different domains are highly desired in cross-domain recommendation tasks because they contain general information covering various domains with structured data format. However, there are few datasets containing KG information for CDR tasks, so in order to enrich the available data resource, we build a KG-enhanced cross-domain recommendation dataset, named Amazon-KG, based on the widely used Amazon dataset for CDR and the well-known KG DBpedia. In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. Finally, we perform quantitative statistical analysis on the dataset. We believe that datasets like Amazon-KG contribute to the development of knowledge-aware cross-domain recommender systems. Our dataset has been released at https://github.com/WangYuhan-0520/Amazon-KG-v2.0-dataset.

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user’s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user’s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.

Recommender System provides users with online services in a personalized way. The performance of traditional recommender systems may deteriorate because of problems such as cold-start and data sparsity. Cross-domain Recommendation System utilizes the richer information from auxiliary domains to guide the task in the target domain. However, direct knowledge transfer may lead to a negative impact due to data heterogeneity and feature mismatch between domains. In this article, we innovatively explore the cross-domain correlation from the perspectives of content semanticity and structural connectivity to fully exploit the information of Knowledge Graph. First, we adopt domain adaptation that automatically extracts transferable features to capture cross-domain semantic relations. Second, we devise a knowledge-aware graph neural network to explicitly model the high-order connectivity across domains. Third, we develop feature fusion strategies to combine the advantages of semantic and structural information. By simulating the cold-start scenario on two real-world datasets, the experimental results show that our proposed method has superior performance in accuracy and diversity compared with the SOTA methods. It demonstrates that our method can accurately predict users’ expressed preferences while exploring their potential diverse interests.

Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.

Adaptive traffic signal control is an important and challenging real-world problem that fits well with the task framework of deep reinforcement learning. As one of the critical design elements, the environmental state plays a crucial role in traffic signal control decisions. The state definitions of most existing works mostly contain lane-level queue length, intersection phase, and other features. However, these works are heuristically designed in representing states. This results in highly sensitive and unstable performances of next actions. The paper proposes a Cooperative Max-Pressure enhanced State Learning for the traffic signal control (CMP-SL), which is inspired by the advanced pressure definition for an intersection in the transportation field to cope with this problem. First, our CMP-SL explicitly extends the cooperative max-pressure to the state definition of a target intersection, aiming to obtain accurate environment information by including the traffic pressures of surrounding intersections. From then on, a graph attention mechanism (GAT) is used to learn the state representation of the target intersection in our spatial-temporal state module. Second, since the state is coupled with the reward in reinforcement learning, our method takes the cooperative max-pressure of the target intersection into the reward definition. Furthermore, a temporal convolutional network (TCN) based sequence model is used to capture the historical state of traffic flow. And the historical spatial-temporal and the current spatial state features are concatenated into a DQN network to predict the Q value and generate each phase action. Finally, experiments with two real-world traffic datasets demonstrate that our method achieves shorter vehicle average times and higher network throughput than the state-of-the-art models.

In social media analysis, gender prediction is one of the most important tasks of user profiling. Web users often post messages in a timeline manner to record their living moments. These messages containing texts and images, constitute long multi-modal data that potentially represents the living style, preference, or opinion regarding users. Therefore, it is feasible to predict the gender of a user by utilizing such living moments. However, the rich modalities (time, length, text, and image) of living moments with difficult challenges have not been fully exploited by the research communities for practical applications. To this end, we propose a novel gender prediction framework based on user-posted living Moments MomNet). The MomNet mainly consists of a moment memory module and a central executive module inspired by the two characteristics of working memory theory. One is that humans can associate related information to facilitate memory. Our moment memory module aggregates similar uni-modal moments of a user to form different chunks and encode the chunks into moment memory representations. The other is that humans coordinate information from different modalities to make judgments. Our central executive module is designed to coordinate comprehensive attentions of moment memory representations from texts, images, and their combinations. Finally, a softmax classifier is used to predict gender. Extensive experiments conducted on a real-world public dataset show that our framework achieves 86.63% accuracy and outperforms all state-of-the-art methods in terms of accuracy.

In real traffic scenarios, the changes of vehicle resolution that the camera captures tend to be relatively obvious considering the distances to the vehicle, different directions, and height of the camera. When the resolution difference exists between the probe and the gallery vehicle, the resolution mismatch will occur, which will seriously influence the performance of the vehicle re-identification (Re-ID). This problem is also known as multi-resolution vehicle Re-ID. An effective strategy is equivalent to utilize image super-resolution to handle the resolution gap. However, existing methods conduct super-resolution on global images instead of local representation of each image, leading to much more noisy information generated from the background and illumination variations. In our work, a local-enhanced multi-resolution representation learning (LMRL) is therefore proposed to address these problems by combining the training of local-enhanced super-resolution (LSR) module and local-guided contrastive learning (LCL) module. Specifically, we use a parsing network to parse a vehicle into four different parts to extract local-enhanced vehicle representation. And then, the LSR module, which consists of two auto-encoders that share parameters, transforms low-resolution images into high-resolution in both global and local branches. LCL module can learn discriminative vehicle representation by contrasting local representation between the high-resolution reconstructed image and the ground truth. We evaluate our approach on two public datasets that contain vehicle images at a wide range of resolutions, in which our approach shows significant superiority to the existing solution.

Recent advancements in convolutional neural networks based object detection have enabled analyzing the mounting video data with high accuracy. However, inference speed is a major drawback of these video analysis system because of the heavy object detectors. To address the computational and practicability challenges of video analysis, we propose FastQ, a system for efficient querying over video at scale. Given a target video, FastQ can automatically label the category and number of objects for each frame. We introduce a novel lightweight object detector named FDet to improve the efficiency of query system. First, a difference detector filters the frames whose difference is less than the threshold. Second, FDet is employed to efficiently label the remaining frames. To reduce inference time, FDet detects a center keypoint and a pair of corners from the feature map generated by a lightweight backbone to predict the bounding boxes. FDet completely avoid the complicated computation related to anchor boxes. Compared with state-of-the-art real-time detectors, FDet achieves superior performance with 29.1% AP on COCO benchmark at 25.3ms. Experiments show that FastQ achieves 150 times to 300 times speed-ups while maintaining more than 90% accuracy in video queries.

With explosive surveillance data during day and night, visible-infrared person re-identification (VI-ReID) is an emerging challenge due to the apparent cross-modality discrepancy between visible and infrared images. Existing VI-ReID work mainly focuses on learning a robust feature to represent a person in both modalities despite the modality gap cannot be effectively eliminated. Recent research works have proposed various generative adversarial network (GAN) models to transfer the visible modality to another unified modality, aiming to bridge the cross-modality gap. However, they neglect the information loss caused by transferring the domain of visible images which is significant for identification. To effectively address the problems, we observe that key information such as textures and semantics in an infrared image can help to color the image itself and the colored infrared image maintains rich information from infrared image while reducing the discrepancy with the visible image. We therefore propose a colorization-based Siamese generative adversarial network (CoSiGAN) for VI-ReID to bridge the cross-modality gap, by retaining the identity of the colored infrared image. Furthermore, we also propose a feature-level fusion model to supplement the transfer loss of colorization. The experiments conducted on two cross-modality person re-identification datasets demonstrate the superiority of the proposed method compared with the state-of-the-arts.