ABSTRACT Detecting ritual implements in Thangka paintings—such as swords and scriptures—remains challenging due to their intricate visual composition and symbolic complexity. Existing object detection models, typically trained on natural scenes, tend to perform poorly in this domain. To address this limitation, we summarize the principles of composition in Thangka and identify key spatial and co‐occurrence priors specific to ritual implements. Based on these insights, we propose GPCDet: a guided by principles of composition detector that integrates domain‐specific priors into the detection process. Specifically, we introduce a spatial coordinate attention module to emphasize critical spatial regions where implements frequently appear. In addition, we design a graph convolution network‐auxiliary detection module to model inter‐category co‐occurrence, thereby enhancing feature representation and improving classification performance. Experiments on the newly curated ritual implements in Thangka (RITK) dataset show that GPCDet achieves substantial improvements over existing methods, establishing a new state‐of‐the‐art baseline for this challenging task.

As a key problem in the field of recommender systems, Click-Through Rate (CTR) prediction has garnered significant attention due to its pivotal role in industrial applications. In recent years, numerous CTR prediction models have emerged, mainly focusing on feature interactions and user interest modeling. However, existing approaches are one-sided and tend to ignore the respective effects of users’ relatively stable discrete preferences and continuous dynamic interests. In addition, current models usually overlook the decision-making process behind users’ clicks, making the predicted results difficult to interpret. To address these issues, this paper introduces the Decision Evaluation Network driven by User Preferences and Dynamic Interests (UPDI-DEN), which innovatively reframes the CTR prediction task as a problem of evaluating user click decisions. The proposed model exhibits an advanced capability to distinctly capture the discrete preferences and dynamic interests embedded within users’ history sequences, and explicitly model their respective impacts on the decision-making process governing final click behavior. Experimental results demonstrate the effectiveness and strong competitiveness of the proposed method on three datasets.

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge across domains.Disentangled representation learning provides an effective solution to model complex user preferences by separating intra-domain features (domainshared and domain-specific features), thereby enhancing robustness and interpretability.However, disentanglement-based CDR methods employing generative modeling or GNNs with contrastive objectives face two key challenges: (i) pre-separation strategies decouple features before extracting collaborative signals, disrupting intra-domain interactions and introducing noise; (ii) unsupervised disentanglement objectives lack explicit task-specific guidance, resulting in limited consistency and suboptimal alignment.To address these challenges, we propose DGCDR, a GNN-enhanced encoder-decoder framework.To handle challenge (i), DGCDR first applies GNN to extract high-order collaborative signals, providing enriched representations as a robust foundation for disentanglement.The encoder then dynamically disentangles features into domain-shared and -specific spaces, preserving collaborative information during the separation process.To handle challenge (ii), the

In the context of ordinal classification, combining absolute information (examples with class labels) and relative information (couples of examples with preference orders) has recently been shown to be an effective way to build performant models. Usually, sufficient amounts of labelled data are needed to develop good ordinal classification models. In order to reduce the cost and time of labelling, a common alternative is to exploit active learning strategies. In this paper, firstly, we propose a semi-supervised distance metric learning method for ordinal classification with absolute information, relative information and unlabelled data. Subsequently, we propose several active metric learning strategies to select informative and representative examples, couples and triplets. Finally, we test these strategies on some classical ordinal classification datasets. The experimental results show the effectiveness of these strategies.

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).

Recently, GNN(Graph Neural Network) recommender systems have benefitted from contrastive learning as an auxiliary task of recommendation and have employed data augmentation to overcome the data sparsity problem. However, we find that the training process of contrastive learning is affected by the popularity bias due to the longtail distribution of interaction data, resulting in the inadequate feature training of low-degree nodes. To address this problem, we propose DCLGCF (Debiased Contrastive Learning For Graph Collaborative Filtering). More specifically, we propose two data augmentation methods with respect to popularity reduction and longtail enhancement. In addition, we propose Mixed-InfoNCE, which designs a novel mixed sampling strategy and introduce a new contrastive learning loss function by considering a frequency penalty term, aiming at increasing the contribution of longtail items to the gradient calculation, and enhancing the training of longtail item features. To validate the effectiveness of our proposed DCLGCF, we conduct thorough experiments on four real-world datasets. The results clearly demonstrate that DCLGCF outperforms existing models in terms of recommendation accuracy, and remarkable improvements are achieved especially when recommending longtail items.

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.

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.

Recommender systems play an important role in providing personalized information to users and helping address the information overload problem. Recent research has considered social theories and studied the importance of social influence in social recommendation systems. However, many publications ignored the users' roles information or just considered some single roles. In fact, users often have many different roles. Besides, different types of users (users with different roles) might have different conformity tendency. Thus, this inspires us to study how conformity tendency changes with users' roles in recommender systems. We firstly formalize conformity influence by defining a utility function and then propose a probabilistic graphical model integrating both users' roles and conformity tendency, named as Role Conformity Recommender Systems (RCRS). We evaluate the proposed model on several real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches.